[5:1] CFCS: Entropy and Kullback-Leibler Divergence, M. Osborne - University of Edinburg 2008 - http://www.inf.ed.ac.uk/teaching/courses/cfcs1/lectures/entropy.pdf

[5:2] Jensen-Shannon divergence, Wikipedia the free encyclopedia Wikimedia Foundation - https://en.wikipedia.org/wiki/Jensen–Shannon_divergence

[5:3] Machine Learning: A Probabilistic Perspective §2.3.8 Mutual Information, K. Murphy – MIT Press 2012

[5:4] Learning with Bregman Divergences, I. Dhillon, J. Ghosh - University of Texas at Austin - http://www.cs.utexas.edu/users/inderjit/Talks/bregtut.pdf

[5:5] A Tutorial on Principal Components Analysis, L. Smith, - 2002 http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

[5:6] Fast Cross-validation in Robust PCA, S. Engelen, M. Hubert - COMPSTAT 2004 symposium, Partial Least Squares Physica-Verlag/Springer –http://wis.kuleuven.be/stat/robust/papers/2004/fastcvpcaCOMPSTAT2004.pdf

[5:7] A survey of dimension reduction techniques, I. Fodor - Center for Applied Scientific Computing Lawrence Livermore National Laboratory 2002 - https://e-reports-ext.llnl.gov/pdf/240921.pdf

[5:8] Multiple Correspondence Analysis - Wikipedia

https://en.wikipedia.org/wiki/Multiple_correspondence_analysis

[5:9] Dimension Reduction for Fast Similarity Search in Large Time Series Databases. E. Keogh, K. Chakrabarti, M. Pazzani, S. Mehrotra. - Dept. of Information and Computer Science, University of California Irvine 2000 - http://www.ics.uci.edu/~pazzani/Publications/dimen.pdf

[5:10] Manifold learning with applications to object recognition, D Thompson - Carnegie-Mellon University Course AP 6 - https://www.cs.cmu.edu/~efros/courses/AP06/presentations/ThompsonDimensionalityReduction.pdf

[5:11] Manifold learning: Theory and Applications, Y. Ma, Y. Fu - CRC Press 2012