PAMI
2012
12 years 2 months ago
2012
Abstract—We consider two desired properties of learning algorithms: sparsity and algorithmic stability. Both properties are believed to lead to good generalization ability. We sh...
CORR
2012
Springer
12 years 7 months ago
2012
Springer
We present a novel primal-dual analysis on a class of NPhard sparsity minimization problems to provide new interpretations for their well known convex relaxations. We show that th...
ICCV
2011
IEEE
12 years 12 months ago
2011
IEEE
As a recently proposed technique, sparse representation based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse lin...
ICCV
2011
IEEE
12 years 12 months ago
2011
IEEE
Progress in action recognition has been in large part due to advances in the features that drive learning-based methods. However, the relative sparsity of training data and the ri...
ICCV
2011
IEEE
12 years 12 months ago
2011
IEEE
In this work, we propose to use attributes and parts for recognizing human actions in still images. We define action attributes as the verbs that describe the properties of human...
CISS
2010
IEEE
13 years 3 months ago
2010
IEEE
Abstract-Malicious attacks against power system state estimation are considered. It has been recently observed that if an adversary is able to manipulate the measurements taken at ...
ICASSP
2011
IEEE
13 years 3 months ago
2011
IEEE
Sparsity in the eigenspace of signal covariance matrices is exploited in this paper for compression and denoising. Dimensionality reduction (DR) and quantization modules present i...
ICASSP
2011
IEEE
13 years 3 months ago
2011
IEEE
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
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TWC
2010
13 years 6 months ago
2010
We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scal...