Sparse approximation

Results: 86



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11Estimation, Optimization, and Parallelism when Data is Sparse H. Brendan McMahan2 Google, Inc.2 Seattle, WA 98103

Estimation, Optimization, and Parallelism when Data is Sparse H. Brendan McMahan2 Google, Inc.2 Seattle, WA 98103

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Source URL: stanford.edu

Language: English - Date: 2014-09-05 13:17:03
12ALGORITHMS FOR SIMULTANEOUS SPARSE APPROXIMATION PART I: GREEDY PURSUIT JOEL A. TROPP, ANNA C. GILBERT, AND MARTIN J. STRAUSS Abstract. A simultaneous sparse approximation problem requests a good approximation of several

ALGORITHMS FOR SIMULTANEOUS SPARSE APPROXIMATION PART I: GREEDY PURSUIT JOEL A. TROPP, ANNA C. GILBERT, AND MARTIN J. STRAUSS Abstract. A simultaneous sparse approximation problem requests a good approximation of several

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Source URL: users.cms.caltech.edu

Language: English - Date: 2007-09-11 17:01:55
13I 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
14IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 10, OCTOBERGreed is Good: Algorithmic Results for Sparse Approximation

IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 10, OCTOBERGreed is Good: Algorithmic Results for Sparse Approximation

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Source URL: users.cms.caltech.edu

Language: English - Date: 2007-09-11 17:01:55
15Inducing Wavelets into Random Fields via Generative Boosting Jianwen Xie, Yang Lu, Song-Chun Zhu, and Ying Nian Wu∗ Department of Statistics, University of California, Los Angeles, USA  Abstract

Inducing Wavelets into Random Fields via Generative Boosting Jianwen Xie, Yang Lu, Song-Chun Zhu, and Ying Nian Wu∗ Department of Statistics, University of California, Los Angeles, USA Abstract

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Source URL: www.stat.ucla.edu

Language: English - Date: 2015-09-11 02:51:37
16DYNAMIC SPARSE STATE ESTIMATION USING ℓ1 -ℓ1 MINIMIZATION: ADAPTIVE-RATE MEASUREMENT BOUNDS, ALGORITHMS AND APPLICATIONS João Mota⋆, Nikos Deligiannis⋆♯, Aswin C. Sankaranarayanan†, Volkan Cevher‡ , Miguel

DYNAMIC SPARSE STATE ESTIMATION USING ℓ1 -ℓ1 MINIMIZATION: ADAPTIVE-RATE MEASUREMENT BOUNDS, ALGORITHMS AND APPLICATIONS João Mota⋆, Nikos Deligiannis⋆♯, Aswin C. Sankaranarayanan†, Volkan Cevher‡ , Miguel

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Source URL: www.ee.ucl.ac.uk

Language: English - Date: 2015-02-11 09:51:28
17ALGORITHMS 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
18MONAURAL SEPARATION AND CLASSIFICATION OF NON-LINEAR TRANSFORMED INDEPENDENT SIGNALS: AN SVM PERSPECTIVE Sepp Hochreiter and Michael C. Mozer Department of Computer Science University of Colorado Boulder, CO 80309

MONAURAL SEPARATION AND CLASSIFICATION OF NON-LINEAR TRANSFORMED INDEPENDENT SIGNALS: AN SVM PERSPECTIVE Sepp Hochreiter and Michael C. Mozer Department of Computer Science University of Colorado Boulder, CO 80309

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Source URL: www.bioinf.jku.at

Language: English - Date: 2013-01-23 02:26:38
19Mach Learn:3–39 DOIs10994A majorization-minimization approach to the sparse generalized eigenvalue problem Bharath K. Sriperumbudur · David A. Torres ·

Mach Learn:3–39 DOIs10994A majorization-minimization approach to the sparse generalized eigenvalue problem Bharath K. Sriperumbudur · David A. Torres ·

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Source URL: eceweb.ucsd.edu

Language: English - Date: 2015-07-31 19:00:27
201030  IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 3, MARCH 2006 Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise

1030 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 3, MARCH 2006 Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise

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Source URL: users.cms.caltech.edu

Language: English - Date: 2007-09-11 17:01:58