Title: | Advances in independent component analysis and nonnegative matrix factorization |
Author(s): | Yuan, Zhijian |
Date: | 2009 |
Language: | en |
Pages: | Verkkokirja (759 KB, 90 s.) |
Department: | Tietojenkäsittelytieteen laitos |
ISBN: | 978-951-22-9831-0 (electronic) 978-951-22-9830-3 (printed) |
Series: | Dissertations in information and computer science / Helsinki University of Technology,, 13 |
ISSN: | 1797-5069 |
Subject: | Computer science |
Keywords: | independent component analysis, FastICA algorithms, nonnegative matrix factorization |
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Abstract:A fundamental problem in machine learning research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis (PCA), factor analysis, and projection pursuit. In this thesis, we consider two popular and widely used techniques: independent component analysis (ICA) and nonnegative matrix factorization (NMF).
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Parts:[Publication 1]: Zhijian Yuan and Erkki Oja. 2004. A FastICA algorithm for non-negative independent component analysis. In: Carlos G. Puntonet and Alberto Prieto (editors). Proceedings of the 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2004). Granada, Spain. 22-24 September 2004. Springer. Lecture Notes in Computer Science, volume 3195, pages 1-8.[Publication 2]: Scott C. Douglas, Zhijian Yuan, and Erkki Oja. 2006. Average convergence behavior of the FastICA algorithm for blind source separation. In: Justinian Rosca, Deniz Erdogmus, José C. Príncipe, and Simon Haykin (editors). Proceedings of the 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006). Charleston, SC, USA. 5-8 March 2006. Springer. Lecture Notes in Computer Science, volume 3889, pages 790-798.[Publication 3]: Erkki Oja and Zhijian Yuan. 2006. The FastICA algorithm revisited: convergence analysis. IEEE Transactions on Neural Networks, volume 17, number 6, pages 1370-1381.[Publication 4]: Zhijian Yuan and Erkki Oja. 2005. Projective nonnegative matrix factorization for image compression and feature extraction. In: Heikki Kalviainen, Jussi Parkkinen, and Arto Kaarna (editors). Proceedings of the 14th Scandinavian Conference on Image Analysis (SCIA 2005). Joensuu, Finland. 19-22 June 2005. Springer. Lecture Notes in Computer Science, volume 3540, pages 333-342.[Publication 5]: Zhirong Yang, Zhijian Yuan, and Jorma Laaksonen. 2007. Projective non-negative matrix factorization with applications to facial image processing. International Journal of Pattern Recognition and Artificial Intelligence, volume 21, number 8, pages 1353-1362.[Publication 6]: Zhijian Yuan and Erkki Oja. 2007. A family of modified projective nonnegative matrix factorization algorithms. In: Mohammed Al-Mualla (editor). Proceedings of the 9th International Symposium on Signal Processing and Its Applications (ISSPA 2007). Sharjah, United Arab Emirates. 12-15 February 2007, pages 1-4.[Publication 7]: Zhijian Yuan, Zhirong Yang, and Erkki Oja. Projective nonnegative matrix factorization: sparseness, orthogonality, and clustering. Submitted to a journal. |
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