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» Parallel matrix algorithms and applications
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CACM
2010
104views more  CACM 2010»
15 years 4 months ago
Faster dimension reduction
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pix...
Nir Ailon, Bernard Chazelle
ICASSP
2011
IEEE
14 years 8 months ago
Co-clustering as multilinear decomposition with sparse latent factors
The K-means clustering problem seeks to partition the columns of a data matrix in subsets, such that columns in the same subset are ‘close’ to each other. The co-clustering pr...
Evangelos E. Papalexakis, Nicholas D. Sidiropoulos
CONSTRAINTS
2011
14 years 8 months ago
CP and IP approaches to cancer radiotherapy delivery optimization
Abstract. We consider the problem of decomposing an integer matrix into a positively weighted sum of binary matrices that have the consecutive-ones property. This problem is well-k...
Davaatseren Baatar, Natashia Boland, Sebastian Bra...
ICASSP
2011
IEEE
14 years 7 months ago
Weighted and structured sparse total least-squares for perturbed compressive sampling
Solving linear regression problems based on the total least-squares (TLS) criterion has well-documented merits in various applications, where perturbations appear both in the data...
Hao Zhu, Georgios B. Giannakis, Geert Leus
JACM
2011
152views more  JACM 2011»
14 years 7 months ago
Robust principal component analysis?
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component i...
Emmanuel J. Candès, Xiaodong Li, Yi Ma, Joh...