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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

Isomap Algorithm isomap.stanford/ Yuri Barseghyan Yasser Essiarab

Isomap Algorithm http://isomap.stanford.edu/ Yuri Barseghyan Yasser Essiarab. Linear Methods for Dimensionality Reduction PCA (Principal Component Analysis): rotate data so that principal axes lie in direction of maximum variance

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280 views • 25 slides

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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CSC 2535: 2013 Lecture 11 Non-linear dimensionality reduction

Isomap Algorithm isomap.stanford/ Yuri Barseghyan Yasser Essiarab

CSC321: Lecture 25: Non-linear dimensionality reduction