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PAMI
2012
8 years 10 hour ago
Sparse Algorithms Are Not Stable: A No-Free-Lunch Theorem
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...
Huan Xu, Constantine Caramanis, Shie Mannor
CORR
2012
Springer
224views Education» more  CORR 2012»
8 years 5 months ago
On the Lagrangian Biduality of Sparsity Minimization Problems
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...
Dheeraj Singaraju, Ehsan Elhamifar, Roberto Tron, ...
ICCV
2011
IEEE
8 years 9 months ago
Sparse Representation or Collaborative Representation: Which Helps Face Recognition?
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...
Lei Zhang, Meng Yang, Xiangchu Feng
ICCV
2011
IEEE
8 years 9 months ago
Feature Seeding for Action Recognition
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...
Pyry Matikainen, Rahul Sukthankar, Martial Hebert
ICCV
2011
IEEE
8 years 9 months ago
Human Action Recognition by Learning Bases of Action Attributes and Parts
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...
Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy La...
CISS
2010
IEEE
9 years 1 months ago
Limiting false data attacks on power system state estimation
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 ...
Oliver Kosut, Liyan Jia, Robert J. Thomas, Lang To...
ICASSP
2011
IEEE
9 years 1 months ago
Eigenspace sparsity for compression and denoising
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...
Ioannis D. Schizas, Georgios B. Giannakis
ICASSP
2011
IEEE
9 years 1 months ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
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...
Hanchao Qi, Shannon Hughes

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Laurent DuvalResearch Scientist, PhD
IFP Energies nouvelles
Laurent Duval
TWC
2010
9 years 4 months ago
Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering
We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scal...
Chong Luo, Feng Wu, Jun Sun, Chang Wen Chen
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