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15
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How to choose W in Pearson's PCA derivation?

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#matrix-decomposition #trace #svd #singular-values #principal-component-analysis
136
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Is only one SVD computation enough to perform PCA on a matrix and its transpose?

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#matrix-decomposition #svd #transpose #principal-component-analysis
53
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Explanation regarding eigenvectors and eigenvalues

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#linear-algebra #matrices #eigenvalues-eigenvectors #principal-component-analysis
15
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Expressing the discriminant function in terms of dimensionality and complexity.

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#machine-learning #principal-component-analysis
340
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Proof of the Frobenius Norm is the trace

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#linear-algebra #matrices #normed-spaces #machine-learning #principal-component-analysis
58
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What can I learn about the columns with highest variance of a matrix $M\approx L^TR$ from looking at $L$?

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#linear-algebra #probability-theory #matrix-decomposition #variance #principal-component-analysis
235
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Why is the first eigenvalue on the principal component always superior to the second eigenvalue?

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#matrices #statistics #eigenvalues-eigenvectors #principal-component-analysis
778
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Maximising the variance in PCA - Trace Proof

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#matrices #machine-learning #orthogonality #trace #principal-component-analysis
49
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Using symmetry of the dot product, how can I prove this?

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#matrices #vector-spaces #projection #principal-component-analysis
205
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Why does singular value decomposition simultaneously diagonalize a symmetric matrix and its square?

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#eigenvalues-eigenvectors #diagonalization #symmetric-matrices #svd #principal-component-analysis
163
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Start with PCA and multiple regression or Start with multiple regression and PCA

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#statistics #regression #data-analysis #principal-component-analysis
394
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Harris corner detection vs. PCA

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#image-processing #computer-vision #principal-component-analysis
51
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Question about the FG algorithm (convex optimization) (Flury 1984)

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#convex-optimization #lagrange-multiplier #diagonalization #principal-component-analysis
35
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expectation of covariance matrix/singular decomposition matrix

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#matrices #statistics #covariance #principal-component-analysis
146
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Simple, intuitive high school level explanation of spectral theorem and the covariance matrix

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#linear-algebra #statistics #eigenvalues-eigenvectors #linear-transformations #principal-component-analysis

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