Low-rank approximation

Results: 47



#Item
1A Quadratically Convergent Algorithm for Structured Low-Rank Approximation ´ Eric Schost1 and Pierre-Jean Spaenlehauer2 1

A Quadratically Convergent Algorithm for Structured Low-Rank Approximation ´ Eric Schost1 and Pierre-Jean Spaenlehauer2 1

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Source URL: cs.uwaterloo.ca

Language: English - Date: 2015-10-14 23:46:10
    2An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation David Anderson * 1 Ming Gu * 1  Abstract

    An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation David Anderson * 1 Ming Gu * 1 Abstract

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    Source URL: proceedings.mlr.press

    - Date: 2018-02-06 15:06:57
      3Low-Rank Tensor Approximation with Laplacian Scale Mixture Modeling for Multiframe Image Denoising Weisheng Dong Xidian University, China  Guangyu Li

      Low-Rank Tensor Approximation with Laplacian Scale Mixture Modeling for Multiframe Image Denoising Weisheng Dong Xidian University, China Guangyu Li

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      Source URL: www.cv-foundation.org

      - Date: 2015-10-24 15:00:46
        4ridge leverage scores for low-rank matrix approximation Michael B. Cohen, Cameron Musco, Christopher Musco Massachusetts Institute of Technology

        ridge leverage scores for low-rank matrix approximation Michael B. Cohen, Cameron Musco, Christopher Musco Massachusetts Institute of Technology

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        Source URL: www.chrismusco.com

        - Date: 2016-11-15 09:52:34
          5JMLR: Workshop and Conference Proceedings vol 40:1–29, 2015  On the Complexity of Learning with Kernels Nicol`o Cesa-Bianchi  NICOLO . CESA - BIANCHI @ UNIMI . IT

          JMLR: Workshop and Conference Proceedings vol 40:1–29, 2015 On the Complexity of Learning with Kernels Nicol`o Cesa-Bianchi NICOLO . CESA - BIANCHI @ UNIMI . IT

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          Source URL: jmlr.org

          Language: English - Date: 2015-07-20 20:08:35
          6I will discuss recent work on randomized algorithms for low-rank approximation and principal component analysis (PCA). The talk will focus on efforts that move beyond the extremely fast, but relatively crude approximatio

          I will discuss recent work on randomized algorithms for low-rank approximation and principal component analysis (PCA). The talk will focus on efforts that move beyond the extremely fast, but relatively crude approximatio

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          Source URL: mmds-data.org

          Language: English - Date: 2016-06-23 15:50:48
          7Neural Word Embedding as Implicit Matrix Factorization Omer Levy Department of Computer Science Bar-Ilan University

          Neural Word Embedding as Implicit Matrix Factorization Omer Levy Department of Computer Science Bar-Ilan University

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          Source URL: papers.nips.cc

          Language: English - Date: 2014-12-02 20:43:40
          8I will discuss recent work on randomized algorithms for low-rank approximation and principal component analysis (PCA). The talk will focus on efforts that move beyond the extremely fast, but relatively crude approximatio

          I will discuss recent work on randomized algorithms for low-rank approximation and principal component analysis (PCA). The talk will focus on efforts that move beyond the extremely fast, but relatively crude approximatio

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          Source URL: mmds-data.org

          Language: English - Date: 2016-06-23 15:50:48
          9ALGORITHMS FOR SIMULTANEOUS SPARSE APPROXIMATION PART II: CONVEX RELAXATION JOEL A. TROPP Abstract. A simultaneous sparse approximation problem requests a good approximation of several input signals at once using differe

          ALGORITHMS FOR SIMULTANEOUS SPARSE APPROXIMATION PART II: CONVEX RELAXATION JOEL A. TROPP Abstract. A simultaneous sparse approximation problem requests a good approximation of several input signals at once using differe

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          Source URL: users.cms.caltech.edu

          Language: English - Date: 2007-09-11 17:01:57
          10Low Rank Approximation and Regression in Input Sparsity Time David Woodruff IBM Almaden Joint work with Ken Clarkson (IBM Almaden)

          Low Rank Approximation and Regression in Input Sparsity Time David Woodruff IBM Almaden Joint work with Ken Clarkson (IBM Almaden)

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          Source URL: mmds-data.org

          Language: English - Date: 2016-03-09 01:30:25