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NIPS
2007
13 years 9 months ago
Sparse Overcomplete Latent Variable Decomposition of Counts Data
An important problem in many fields is the analysis of counts data to extract meaningful latent components. Methods like Probabilistic Latent Semantic Analysis (PLSA) and Latent ...
Madhusudana V. S. Shashanka, Bhiksha Raj, Paris Sm...
ICASSP
2011
IEEE
12 years 11 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
AIRS
2006
Springer
13 years 11 months ago
Improving Re-ranking of Search Results Using Collaborative Filtering
Search Engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and the context of ...
U. Rohini, Vamshi Ambati
CSDA
2010
132views more  CSDA 2010»
13 years 8 months ago
Approximate low-rank factorization with structured factors
An approximate rank revealing factorization problem with structure constraints on the normalized factors is considered. Examples of structure, motivated by an application in micro...
Ivan Markovsky, Mahesan Niranjan
CISS
2010
IEEE
12 years 11 months ago
Turbo reconstruction of structured sparse signals
—This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, the support of the signal coefficients is parameterized by h...
Philip Schniter