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Nonnegative Matrix Factorization via Rank-one Downdate
Ali Ghodsi Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science University of Waterloo Joint work with Stephen Vavasis and Michael Biggs
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Nonnegative Matrix Factorization
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2 by 1965 560 by 1965 560 by 2 -2.19 -0.02 -3.19 1.02 20 by 28 20 by 28 2 by 1 2 by 1
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Singular Value Decomposition (SVD)
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History
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History
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History
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History
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History (Algorithms)
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History (Algorithms)
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First observation
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Power method Computes the leading singular vectors/value (or eigenvector/value) of a matrix 1 2 while not converged 3 4 5 6 end
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Naive approach to NMF using this observation
1 2 3 4 5 for all set 6 end for Without step 5, this will simply compute the SVD (Jordan's algorithm, Camille Jordan )
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Rank-one Downdata (R1D)
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Objective function
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ApproxRankOneSubmatrix(A)
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Modified power iteration: Demo
Rank-1 submatrix A = Rank-1 submatrix
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Modified power iteration: Demo
v: Rank-1 submatrix Rank-1 submatrix
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Modified power iteration: Demo
v: Rank-1 submatrix Rank-1 submatrix
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Modified power iteration: Demo
v: Rank-1 submatrix Rank-1 submatrix
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Modified power iteration: Demo
v: 0.16 0.21 0.22 0.44 0.74 0.20 Rank-1 submatrix u: Rank-1 submatrix
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Modified power iteration: Demo
v: 0.0 0.44 0.74 0.20 Rank-1 submatrix u: Rank-1 submatrix
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Modified power iteration: Demo
v: 0.0 0.44 0.74 0.20 Rank-1 submatrix u: Rank-1 submatrix
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Modified power iteration: Demo
v: 0.0 0.44 0.74 0.20 Rank-1 submatrix u: Rank-1 submatrix
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Modified power iteration: Demo
v: 0.0 0.44 0.74 0.20 Rank-1 submatrix u: Rank-1 submatrix
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Modified power iteration: Demo
v: 0.0 0.44 0.74 0.20 Rank-1 submatrix u: Rank-1 submatrix Zero-out!
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Modified power iteration: Demo
Rank-1 submatrix Anew =
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Rank-one Downdata (R1D)
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A simple model for text
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Generating a corpus in the model
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Theorem about text
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LSI
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R1D
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Theorem about images
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Experimental results
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LSI
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NMF-DIV
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R1D
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LSI
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NMF_DIV
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R1D
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Thank you!
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