14ica-411 - CSC411Fall2013 MachineLearning&DataMining...

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CSC411 Fall 2013 Machine Learning & Data Mining Lecture 14: Factor Analysis & Independent Components Analysis All lecture slides will be available as .pdf at www.cs.toronto.edu/~zemel/Courses/csc411.html Some of the figures are provided by Chris Bishop from his textbook: ”Pattern Recognition and Machine Learning” and by Kevin Murphy in his book “Machine Learning: A Probabilistic Perspective”
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Return to Graphical Model View Last time we discussed PCA – considered as projection from inputs to reduced dimensional representation Today: as latent variable model Latent variables in mixture models are multinomials (referring to cluster identity). Today we’ll consider continuous latent variables Hidden cause Visible effect
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Genera4ve View Each data example generated by first selecting a point from a distribution in the latent space, then generating a point from the conditional distribution in the input space Mixture models have multinomial latents
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