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CSC 2535: 2013 Lecture 11 Non-linear dimensionality reduction

CSC 2535: 2013 Lecture 11 Non-linear dimensionality reduction

CSC 2535: 2013 Lecture 11 Non-linear dimensionality reduction. Geoffrey Hinton. Dimensionality reduction: Some Assumptions. High-dimensional data often lies on or near a much lower dimensional, curved manifold. A good way to represent data points is by their low-dimensional coordinates.

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975 views • 85 slides
So far:

So far:

So far:. Historical introduction Mathematical background (e.g., pattern classification, acoustics) Feature extraction for speech recognition (and some neural processing) What sound units are typically defined

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516 views • 42 slides
Alexandra Moshou , Panayotis Papadimitriou, Konstantinos Makropoulos

Alexandra Moshou , Panayotis Papadimitriou, Konstantinos Makropoulos

European Seismological Commission ESC 2008, 31st General Assembly. Waveform Inversion using teleseismic and regional data for earthquakes that occurred in Greece. Alexandra Moshou , Panayotis Papadimitriou, Konstantinos Makropoulos. Hersonissos, Crete, Greece, 7-12 September 2008.

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388 views • 38 slides
CSC321: Extra Lecture (not on the exam) Non-linear dimensionality reduction

CSC321: Extra Lecture (not on the exam) Non-linear dimensionality reduction

CSC321: Extra Lecture (not on the exam) Non-linear dimensionality reduction. Geoffrey Hinton. Linear methods of reducing dimensionality. PCA finds the directions that have the most variance.

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225 views • 22 slides
CSC321: Lecture 25: Non-linear dimensionality reduction

CSC321: Lecture 25: Non-linear dimensionality reduction

CSC321: Lecture 25: Non-linear dimensionality reduction. Geoffrey Hinton. Linear methods of reducing dimensionality. PCA finds the directions that have the most variance. By representing where each datapoint is along these axes, we minimize the squared reconstruction error.

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212 views • 21 slides

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