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Machine Learning Group

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Department of Computer Science

University of Copenhagen

 

Sigurdsgade 41

2200 København N

Denmark

 

Office: 1.08

 

EMail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

 

Publications

Journals

  1. Robert Beck, Chieh A Lin, Emille O Ishida, Fabian Gieseke, Rafel S Souza, Marcus V Costa-Duarte, Mohammed W Hattab, and Alberto Krone-Martins. On the realistic validation of photometric redshifts. Monthly Notices of the Royal Astronomical Society (MNRAS), 2017.  Accepted 

  2. Fabian Gieseke, Cosmin Oancea, and Christian Igel. bufferkdtree: A Python library for massive nearest neighbor queries on multi-many-core devices. Knowledge-Based Systems 120:1–3, 2017.   

  3. Kristoffer Stensbo-Smidt, Fabian Gieseke, Andrew Zirm, Kim Steenstrup Pedersen, and Christian Igel. Sacrificing information for the greater good: how to select photometric bands for optimal accuracy. Monthly Notices of the Royal Astronomical Society (MNRAS) 464(3):2577-2596, 2017.   

  4. Jan Kremer, Kristoffer Stensbo-Smidt, Fabian Gieseke, Kim Steenstrup Pedersen, and Christian Igel. Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy. IEEE Intelligent Systems, 2016.  Accepted. 

  5. Michele Sasdelli, E O Ishida, R Vilalta, M Aguena, V C Busti, H Camacho, A M M Trindade, Fabian Gieseke, R S Souza, Y T Fantaye, and P A Mazzali. Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: A machine learning approach. Monthly Notices of the Royal Astronomical Society (MNRAS) 461(2):2044-2059, 2016.   

  6. Jan Kremer, Fabian Gieseke, Kim Steenstrup Pedersen, and Christian Igel. Nearest Neighbor Density Ratio Estimation for Large-Scale Applications in Astronomy. Astronomy and Computing 12:62–72, 2015.   

  7. Fabian Gieseke, Antti Airola, Tapio Pahikkala, and Oliver Kramer. Fast and Simple Gradient-Based Optimization for Semi-Supervised Support Vector Machines. Neurocomputing (ICPRAM 2012 Special Issue) 123(10):23-32, 2014.   

  8. Tapio Pahikkala, Antti Airola, Fabian Gieseke, and Oliver Kramer. On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers. Journal of Computer Science and Technology (ICDM 2012 Special Issue) 29(1):90–104, 2014.   

  9. Fabian Gieseke. From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy. KI – Künstliche Intelligenz (abstract of my PhD thesis) 27(3):281-285, 2013.   

  10. Oliver Kramer, Fabian Gieseke, and Kai Lars Polsterer. Learning Morphological Maps of Galaxies with Unsupervised Regression. Expert Systems with Applications 40(8):2841-2844, 2013.   

  11. Kai Lars Polsterer, Peter Zinn, and Fabian Gieseke. Finding New High-Redshift Quasars by Asking the Neighbours. Monthly Notices of the Royal Astronomical Society (MNRAS) 428(1):226-235, 2013.   

  12. Oliver Kramer, Fabian Gieseke, and Benjamin Satzger. Wind Energy Prediction and Monitoring with Neural Computation. Neurocomputing 109(0):84-93, 2013.   

  13. Fabian Gieseke, Gabriel Moruz, and Jan Vahrenhold. Resilient K-d Trees: K-Means in Space Revisited. Frontiers of Computer Science (ICDM 2010 Special Issue) 6(2):166-178, 2012.   

  14. Oliver Kramer and Fabian Gieseke. Evolutionary Kernel Density Regression. Expert Systems with Applications 10(39):9246-9254, 2012.   

  15. Fabian Gieseke, Oliver Kramer, Antti Airola, and Tapio Pahikkala. Efficient Recurrent Local Search Strategies for Semi- and Unsupervised Regularized Least-Squares Classification. Evolutionary Intelligence 5(3):189-205, 2012.   

  16. Fabian Gieseke, Joachim Gudmundsson, and Jan Vahrenhold. Pruning Spanners and Constructing Well-Separated Pair Decompositions in the Presence of Memory Hierarchies. Journal of Discrete Algorithms (JDA) 8(3):259-272, 2010.   

 

Peer-Reviewed Conference and Workshop Contributions

  1. Corneliu Florea, Cosmin Toca, and Fabian Gieseke. Artistic movement recognition by boosted fusion of color structure and topographic description. In IEEE Winter Conference on Applications of Computer Vision. 2017.  Accepted 

  2. Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahaba, Christian Igel, and Tom Heskes. Bigger Buffer k-d Trees on Multi-Many-Core Systems. In Workshop on Big Data & Deep Learning in HPC. 2016, 172-180.  draft 

  3. Kai Lars Posterer, Fabian Gieseke, Christian Igel, Bernd Doser, and Nikos Gianniotis. Parallelized rotation and flipping INvariant Kohonen maps (PINK ) on GPUs. In Proceedings of the 24nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2016, 405-410.   

  4. Fabian Gieseke, Tapio Pahikkala, and Tom Heskes. Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. In Proceedings of the 14th International Symposium on Intelligent Data Analysis. Advances in Intelligent Data Analysis XIV 9385. 2015, 95–107.   

  5. Fabian Gieseke. An Efficient Many-Core Implementation for Semi-Supervised Support Vector Machines. In International Workshop on Machine Learning, Optimization, and Big Data (MOD2015). 2015, 145–157.   

  6. Oliver Kramer, Fabian Gieseke, Justin Heinermann, Jendrik Poloczek, and Nils Treiber. A Framework for Data Mining in Wind Power Time Series. In Proceedings of the 2nd ECML/PKDD 2014 International Workshop on Data Analytics for Renewable Energy Integration (DARE'14), Lecture Notes in Compute Science 8817. 2014, 97-107.   

  7. Fabian Gieseke, Justin Heinermann, Cosmin Oancea, and Christian Igel. Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. In Proceedings of the 31st International Conference on Machine Learning (ICML) 32(1). 2014, 172-180.   

  8. Fabian Gieseke, Kai Lars Posterer, Cosmin Oancea, and Christian Igel. Speedy Greedy Feature Selection: Better Redshift Estimation via Massive Parallelism. In Proceedings of the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2014, 87–92.   

  9. Fabian Gieseke, Tapio Pahikkala, and Christian Igel. Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification. In Proceedings of the 5th Asian Conference on Machine Learning (ACML). 2013, 62-71.   

  10. Justin Heinermann, Oliver Kramer, Kai Lars Polsterer, and Fabian Gieseke. On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy. In KI 2013: Advances in Artificial Intelligence. Lecture Notes in Computer Science series, volume 8077, Springer, 2013, pages 86-97.   

  11. Oliver Kramer, Nils Treiber, and Fabian Gieseke. Machine Learning in Wind Energy Information Systems. In EnviroInfo. 2013, 16-24. 

  12. Fabian Gieseke and Oliver Kramer. Towards Non-Linear Constraint Estimation for Expensive Optimization. In EvoApplications. 2013, 459-468.   

  13. Tapio Pahikkala, Antti Airola, Fabian Gieseke, and Oliver Kramer. Unsupervised Multi-Class Regularized Least-Squares Classification. In Proceedings of the 12th IEEE International Conference on Data Mining (ICDM). 2012, 585-594.   

  14. Fabian Gieseke, Antti Airola, Tapio Pahikkala, and Oliver Kramer. Sparse Quasi-Newton Optimization for Semi-Supervised Support Vector Machines. In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM). 2012, 45-54.   

  15. Oliver Kramer and Fabian Gieseke. Short-Term Wind Energy Forecasting Using Support Vector Regression. In Proceedings of the International Conference on Soft Computing Models in Industrial and Environmental Applications. 2011, 271-280.   

  16. Fabian Gieseke, Oliver Kramer, Antti Airola, and Tapio Pahikkala. Speedy Local Search for Semi-Supervised Regularized Least-Squares. In Proceedings of the 34th Annual German Conference on Artificial Intelligence. 2011, 87-98.   

  17. Oliver Kramer and Fabian Gieseke. Variance Scaling for EDAs Revisited. In Proceedings of the 34th Annual German Conference on Artificial Intelligence. 2011, 169-178.   

  18. Oliver Kramer and Fabian Gieseke. Analysis of wind energy time series with kernel methods and neural networks. In Proceedings of the 7th International Conference on Natural Computation. 2011, 2381-2385.   

  19. Fabian Gieseke, Gabriel Moruz, and Jan Vahrenhold. Resilient K-d Trees: K-Means in Space Revisited. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM). 2010, 815-820.   

  20. Fabian Gieseke, Kai Lars Polsterer, Andreas Thom, Peter Zinn, Dominik Bomans, Ralf-Jürgen Dettmar, Oliver Kramer, and Jan Vahrenhold. Detecting Quasars in Large-Scale Astronomical Surveys. In Proceedings of the 9th International Conference on Machine Learning and Applications (ICMLA). 2010, 352-357.   

  21. Fabian Gieseke, Tapio Pahikkala, and Oliver Kramer. Fast Evolutionary Maximum Margin Clustering. In Proceedings of the 26th International Conference on Machine Learning (ICML). 2009, 361-368.   

  22. Fabian Gieseke and Jan Vahrenhold. Cache-Oblivious Construction of a Well-Separated Pair Decomposition. In Proceedings of the 25th European Workshop on Computational Geometry. 2009, 341-344.   

  23. Evgeni Tsivtsivadze, Fabian Gieseke, Tapio Pahikkala, Jorma Boberg, and Tapio Salakoski. Learning Preferences with Co-Regularized Least-Squares. In Proceedings of the ECML/PKDD Workshop on Preference Learning. 2008, 52-66.   

 

Theses

  1. Fabian Gieseke. From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy. PhD thesis, Carl von Ossietzky Universität Oldenburg, 2011.   

  2. Fabian Gieseke. Algorithmen zur Konstruktion und Ausdünnung von Spanner-Graphen im Cache-Oblivious-Modell. Diplomarbeit, Westfälische Wilhelms-Universität Münster (in German), 2006.   

 

Other Contributions

  1. Michele Sasdelli, Emille O Ishida, R Vilalta, M Aguena, V C Busti, H Camacho, A M M Trindade, Fabian Gieseke, R S Souza, Y T Fantaye, and P A Mazzali. Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach. CoRR abs/1512.06810, 2015. 

  2. Fabian Gieseke, Cosmin E Oancea, Ashish Mahabal, Christian Igel, and Tom Heskes. Bigger Buffer k-d Trees on Multi-Many-Core Systems. CoRR abs/1512.02831, 2015. 

  3. Kristoffer Stensbo-Smidt, Fabian Gieseke, Chstian Igel, Andrew Zirm, and Kim Steenstrup Pedersen. Simple, Fast and Accurate Photometric Estimation of Specific Star Formation Rate. CoRR abs/1511.05424, 2015. 

  4. Fabian Gieseke. Big Data Analytics in Astronomy... using the supercomputer under your desk!. Big Data in a Transdisciplinary Perspective. 7th Herrenhausen Conference of the Volkswagen Foundation, 25-27 March 2015, Herrenhausen Palace, 2015.   

  5. Kai Polsterer, Fabian Gieseke, and Christian Igel. Automatic Classification of Galaxies via Machine Learning Techniques - Parallelized Rotation/Flipping Invariant Kohonen Map (PINK). In Proceedings of the 24th Annual Astronomical Data Analysis Software & Systems conference (ADASS). 2014.    

  6. Fabian Gieseke. Von uberwachten zu unuberwachten Support-Vektor-Maschinen und Anwendungen in der Astronomie. In Steffen Hölldobler et al. (ed.). Ausgezeichnete Informatikdissertationen 2012. GI-Edition Lecture Notes in Informatics (LNI), D-13 series, Bonner Köllen Verlag (in German), 2013.   

  7. Kai Polsterer, Fabian Gieseke, Christian Igel, and Tomotsugu Goto. Improving the Performance of Photometric Regression Models via Massive Parallel Feature Selection. In Proceedings of the 23rd Annual Astronomical Data Analysis Software & Systems conference (ADASS). 2013.    

  8. Fabian Gieseke, Kai Polsterer, and Peter Zinn. Photometric Redshift Estimation of Quasars: Local versus Global Regression. In Proceedings of the 21st Annual Astronomical Data Analysis Software & Systems conference (ADASS). 2011.    

  9. Kai Polsterer, Fabian Gieseke, and Oliver Kramer. Galaxy Classification without Feature Extraction. In Proceedings of the 21st Annual Astronomical Data Analysis Software & Systems conference (ADASS). 2011.    

  10. Fabian Gieseke, Kai Polsterer, Andreas Thom, Peter Zinn, Dominik Bomans, Ralf-Jürgen Dettmar, Oliver Kramer, and Jan Vahrenhold. Identifying Quasars in Large-Scale Spectroscopic Surveys. Astroinformatics 2010, Poster, Pasadena, 2010.   

  11. Kai Polsterer, Fabian Gieseke, Andreas Thom, Oliver Kramer, Jan Vahrenhold, Dominik Bomans, and Ralf-Jürgen Dettmar. Discriminating Point and Extended Sources with k-Nearest Neighbors. Astroinformatics 2010, Poster, Pasadena, 2010.   

  12. Fabian Gieseke. Regularized Least-Squares for Maximum Margin Clustering and Semi-Supervised Classification. Machine Learning Summer School, Cambridge, UK, 2009. 

  13. Fabian Gieseke. Constructing and Pruning Spanners in the Cache-Oblivious Model. Summer School on Algorithmic Data Analysis (SADA07), Helsinki, Finland, 2007.   

Research

The field of data analysis (e.g., data mining, machine learning, algorithm engineering, ...) has gained more and more attention in recent years. One of the reasons for this phenomenon is the fact that the data volumes have increased dramatically during the last decade, leading to so-called big data-problems. This is the case, for instance, in astronomy, where current and upcoming projects like the Sloan Digital Sky Survey (SDSS) or the Large Synoptic Sky Telescope (LSST) gather and will gather data in the tera- and petabyte range. For such projects, the sheer data volume renders a manual analysis impossible, and this necessitates the use of automatic data analysis tools.

 

The corresponding data-rich scenarios often involve a large number of patterns (e.g., number of galaxy images) and/or a large number of dimensions (e.g., pixels per image). Further, a general lack of "labeled data" can often be observed, since the manual interaction with experts can be very time-consuming. Dealing with these situations usually requires the adaptation of standard data analysis techniques, and this is part of my research. In particular, I am interested in the following research fields/projects:

 

Semi- and Unsupervised Support Vector Machines

The task of classifying patterns is among the most prominent ones in the field of machine learning. Support vector machines depict state-of-the-art tools for this task and have been extended to various learning settings including other supervised learning tasks (e.g., regression or preference learning) but also to so-called semi- and unsupervised scenarios.

 

Among these extensions are, for instance, semi-supervised support vector machines, which take additional unlabeled patterns into account (left: black points). This additional information reveals more information about the "structure" of the data and can lead to models with a better performance.

In some cases, no labeled patterns at all are given. This leads to the so-called maximum margin clustering problem. While being very appealing from a practical point of view, both variants induce difficult combinatorial optimization problems, which renders a direct application of these extensions difficult.

 

Developing efficient optimization schemes for these variants is part of my research; see below for corresponding publications or here for an implemetation. Both support vector machines as well as their extensions can successfully by applied for, e.g., text data, which stems from various application domains like e-commerce or social media.

 

 

Energy Systems

In recent years, there has been a significant increase in energy produced by sustainable resources like wind- and solar power plants. This led to a shift of traditional energy systems to so-called smart grids (i. e., distributed systems of energy suppliers and consumers). While the sustainable energy resources are very appealing from an environmental point of view, their volatileness renders the integration into the overall energy system difficult.

 

For this reason, short-term wind and solar energy prediction systems are essential for balance authorities to schedule spinning reserves and reserve energy. This task can be formalized as regression problem (with patterns based on, e.g., wind turbine measurements), and the resulting models are well-suited for short-term forecasting scenarios, see below for details.

 

Big Data in Astronomy

Modern telescopes and satellites can gather huge amounts of data. Current catalogs, for instance, contain data in the terabyte range; upcoming projects will encompass petabytes of data. On the one hand, this data-rich situation offers the opportunity to make new discoveries like detecting new, distant objects. On the other hand, managing such data volumes can be very difficult and usually leads to problem-specific challenges.

Data mining techniques have been recognized to play an important role for upcoming surveys. Typical tasks in astronomy are, for instance, the classification of stars, galaxies, and quasars, or the estimation of the redshift of galaxies based on image data. Appropriate models are already in use for current catalogs (see, e.g., the preprocessing pipeline of the SDSS). However, obtaining high-quality models for specific tasks can still be a very challenging task.

 

I am involved in the development of redshift estimation models (e.g., regression models) for so-called quasi-stellar radio sources (quasars), which are among the most distant objects that can be observed from Earth. To efficiently process the large data volumes, we make use of spatial data structures (like k-d-trees), which can be applied for various other tasks as well. See the publications below for more details.

 

 

Large-Scale Learning

Given the huge data volumes encountered in, e.g., large-scale text mining scenarios (or the ones in astronomy), the running time needed to build appropirate data mining models (training phase) and the application of the final models (test phase) can depict one of the main bottlenecks during the data analysis process. Further, given data volumes that exceed the capacities of the main memory of standard computer systems, the transfer of data can significantly slow down both phases. A desirable goal is, in general, the reduction of the practical runtime needed for the various stages of overall data analysis process, see below for more information.

     

 

Selected Publications

 

A complete list of my publications can be found here.

 

Semi- and Unsupervised Support Vector Machines

  1. Fabian Gieseke, Tapio Pahikkala, and Christian Igel. Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification. In Proceedings of the 5th Asian Conference on Machine Learning (ACML). 2013, 62-71.   

  2. Fabian Gieseke, Antti Airola, Tapio Pahikkala, and Oliver Kramer. Fast and Simple Gradient-Based Optimization for Semi-Supervised Support Vector Machines. Neurocomputing (ICPRAM 2012 Special Issue) 123(10):23-32, 2014.   

  3. Tapio Pahikkala, Antti Airola, Fabian Gieseke, and Oliver Kramer. Unsupervised Multi-Class Regularized Least-Squares Classification. In Proceedings of the 12th IEEE International Conference on Data Mining (ICDM). 2012, 585-594.   

  4. Fabian Gieseke, Tapio Pahikkala, and Oliver Kramer. Fast Evolutionary Maximum Margin Clustering. In Proceedings of the 26th International Conference on Machine Learning (ICML). 2009, 361-368.   

 

Big Data in Astronomy

  1. Jan Kremer, Fabian Gieseke, Kim Steenstrup Pedersen, and Christian Igel. Nearest Neighbor Density Ratio Estimation for Large-Scale Applications in Astronomy. Astronomy and Computing 12:62–72, 2015.   

  2. Kai Lars Polsterer, Peter Zinn, and Fabian Gieseke. Finding New High-Redshift Quasars by Asking the Neighbours. Monthly Notices of the Royal Astronomical Society (MNRAS) 428(1):226-235, 2013.   

  3. Kai Polsterer, Fabian Gieseke, Christian Igel, and Tomotsugu Goto. Improving the Performance of Photometric Regression Models via Massive Parallel Feature Selection. In Proceedings of the 23rd Annual Astronomical Data Analysis Software & Systems conference (ADASS). 2013.    

  4. Fabian Gieseke, Kai Polsterer, and Peter Zinn. Photometric Redshift Estimation of Quasars: Local versus Global Regression. In Proceedings of the Astronomical Data Analysis Software & Systems (ADASS). 2011.    

  5. Fabian Gieseke, Gabriel Moruz, and Jan Vahrenhold. Resilient K-d Trees: K-Means in Space Revisited. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM). 2010, 815-820.   

  6. Fabian Gieseke, Kai Lars Polsterer, Andreas Thom, Peter Zinn, Dominik Bomans, Ralf-Jürgen Dettmar, Oliver Kramer, and Jan Vahrenhold. Detecting Quasars in Large-Scale Astronomical Surveys. In Proceedings of the 9th International Conference on Machine Learning and Applications (ICMLA). 2010, 352-357.   

 

Energy Systems

  1. Oliver Kramer, Nils Treiber, and Fabian Gieseke. Machine Learning in Wind Energy Information Systems. In EnviroInfo. 2013, 16-24. 

  2. Oliver Kramer, Fabian Gieseke, and Benjamin Satzger. Wind Energy Prediction and Monitoring with Neural Computation. Neurocomputing 109(0):84-93, 2013.   

  3. Oliver Kramer and Fabian Gieseke. Short-Term Wind Energy Forecasting Using Support Vector Regression. In Proceedings of the International Conference on Soft Computing Models in Industrial and Environmental Applications. 2011, 271-280.   

 

Large-Scale Learning

  1. Fabian Gieseke, Justin Heinermann, Cosmin Oancea, and Christian Igel. Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. In Proceedings of the 31st International Conference on Machine Learning (ICML). 2014.  Accepted. 

  2. Fabian Gieseke, Gabriel Moruz, and Jan Vahrenhold. Resilient K-d Trees: K-Means in Space Revisited. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM). 2010, 815-820.   

  3. Fabian Gieseke, Joachim Gudmundsson, and Jan Vahrenhold. Pruning Spanners and Constructing Well-Separated Pair Decompositions in the Presence of Memory Hierarchies. Journal of Discrete Algorithms (JDA) 8(3):259-272, 2010.   

Quasi-Newton Semi-Supervised Support Vector Machines

A quasi-Newton optimization framework implemented in Python using the Numpy and the Scipy packages. This type of model depicts an extension of support vector machines to semi-supervised learning settings with both labeled and unlabeled patterns given in the training phase: In contrast to standard support vector machines (left), the model takes the additional unlabeled patterns (right, black points) into account to reveal more information about the structure of the data. The QN-S3VM framework can handle linear and non-linear kernels. In addition, the special case of sparse data (given a linear kernel) can also be handled efficiently.

 

Source Code

 

The code is free for scientific use. In case you are planning to use (parts of) the software for commercial purposes, please contact me. If you use the code for scientific work, please use the reference(s) below to cite us. The source code can be downloaded here.

The code contains three examples for sparse and non-sparse data set instances. If you find any bugs or if you have problems with the code, feel free to contact us via e-mail.

 

History

 

August 2012: Initial Release

 

References

 

  1. Fabian Gieseke, Antti Airola, Tapio Pahikkala, and Oliver Kramer. Fast and Simple Gradient-Based Optimization for Semi-Supervised Support Vector Machines. Neurocomputing (ICPRAM 2012 Special Issue) 123(10):23-32, 2014.   

  2. Fabian Gieseke, Antti Airola, Tapio Pahikkala, and Oliver Kramer. Sparse Quasi-Newton Optimization for Semi-Supervised Support Vector Machines. In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM 2012). 2012, 45-54.   

 

Disclaimer

 

The implementation is free for non-commercial use only. It is not allowed to redistribute the software without permission of the authors. Further, the authors are not responsible for any implications that stem from its use.

Support Vector Machines

The task of classifying patterns is among the most prominent ones in the field of machine learning. Support vector machines depict state-of-the-art tools for this tasks and have been extended to various learning settings.

 

Among these extensions are, for instance, semi-supervised support vector machines that take additional unlabeled patterns into account (black points). This additional information reveals more information about the structure of the data and can lead to better models. In some cases, no labeled patterns at all are given, which leads to the so-called maximum margin clustering problem.

 

  1. Fabian Gieseke, Tapio Pahikkala, and Christian Igel. Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification. In Proceedings of the 5th Asian Conference on Machine Learning (ACML). 2013, 62-71.   

  2. Fabian Gieseke, Antti Airola, Tapio Pahikkala, and Oliver Kramer. Fast and Simple Gradient-Based Optimization for Semi-Supervised Support Vector Machines. Neurocomputing (ICPRAM 2012 Special Issue) 123(10):23-32, 2014.   

  3. Tapio Pahikkala, Antti Airola, Fabian Gieseke, and Oliver Kramer. Unsupervised Multi-Class Regularized Least-Squares Classification. In Proceedings of the 12th IEEE International Conference on Data Mining (ICDM). 2012, 585-594.   

  4. Fabian Gieseke, Tapio Pahikkala, and Oliver Kramer. Fast Evolutionary Maximum Margin Clustering. In Proceedings of the 26th International Conference on Machine Learning (ICML). 2009, 361-368.