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» Parallel matrix algorithms and applications
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CACM
2010
104views more  CACM 2010»
15 years 6 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 10 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 10 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 10 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 9 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...