Low Rank Tucker Approximation of a Tensor from Streaming Data Madeleine Udell Operations Research and Information Engineering Cornell University Based on joint work with Yiming Sun (Cornell), Yang Guo (UW Madison), Charlene Luo (Columbia), and Joel Tropp (Caltech) April 1, 2019 Madeleine Udell, Cornell. Streaming Tucker Approximation. 1
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Low Rank Tucker Approximation of a Tensor from Streaming Data · Low Rank Tucker Approximation of a Tensor from Streaming Data Madeleine Udell Operations Research and Information
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Low Rank Tucker Approximation of a Tensorfrom Streaming Data
Madeleine Udell
Operations Research and Information EngineeringCornell University
Based on joint work withYiming Sun (Cornell), Yang Guo (UW Madison),
Comments: Error of fixed-rank approximation relative to HOOI for r = 10, I = 300using TRP. Total memory use is ((2k + 1)N + kIN) and (Kr2N + K ∗ r2N−2).Low-rank data uses γ = 0.01, 0.1, 1.
Comments: Video data 2200× 1080× 1980. Classify scenes using k-means on: 1)linear sketch along the time dimension k = 20 (Row 1); 2) The Tucker factor alongthe time dimension, computed via our two pass (Row 2) and one pass (Row 3)sketching algorithm (r , k, s) = (10, 20, 41). 3) The Tucker factor along the timedimension, computed via our one pass (Row 4) sketching algorithm(r , k, s) = (10, 300, 601).
I streaming compression for 〈your research〉?references:
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