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» The Structure of Sparse Resultant Matrices
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156
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BMCBI
2008
142views more  BMCBI 2008»
15 years 3 months ago
Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures
Background: DNA microarray technology allows for the measurement of genome-wide expression patterns. Within the resultant mass of data lies the problem of analyzing and presenting...
Meng Piao Tan, Erin N. Smith, James R. Broach, Chr...
125
Voted
DSSCV
2005
Springer
15 years 9 months ago
A Riemannian Framework for the Processing of Tensor-Valued Images
In this paper, we present a novel framework to carry out computations on tensors, i.e. symmetric positive definite matrices. We endow the space of tensors with an affine-invariant...
Pierre Fillard, Vincent Arsigny, Nicholas Ayache, ...
110
Voted
NIPS
2003
15 years 5 months ago
Limiting Form of the Sample Covariance Eigenspectrum in PCA and Kernel PCA
We derive the limiting form of the eigenvalue spectrum for sample covariance matrices produced from non-isotropic data. For the analysis of standard PCA we study the case where th...
David C. Hoyle, Magnus Rattray
153
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ECCV
2010
Springer
15 years 6 months ago
Optimum Subspace Learning and Error Correction for Tensors
Confronted with the high-dimensional tensor-like visual data, we derive a method for the decomposition of an observed tensor into a low-dimensional structure plus unbounded but spa...
114
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JMLR
2010
119views more  JMLR 2010»
14 years 10 months ago
The Group Dantzig Selector
We introduce a new method -- the group Dantzig selector -- for high dimensional sparse regression with group structure, which has a convincing theory about why utilizing the group...
Han Liu, Jian Zhang 0003, Xiaoye Jiang, Jun Liu