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» Data Separation by Sparse Representations
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RT
2004
Springer
14 years 2 months ago
All-Frequency Relighting of Non-Diffuse Objects using Separable BRDF Approximation
This paper presents a technique, based on pre-computed light transport and separable BRDF approximation, for interactive rendering of non-diffuse objects under all-frequency envir...
Rui Wang 0003, John Tran, David P. Luebke
NIPS
2007
13 years 10 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...
NIPS
2007
13 years 10 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
ISCI
2006
58views more  ISCI 2006»
13 years 9 months ago
Streaming data reduction using low-memory factored representations
Many special purpose algorithms exist for extracting information from streaming data. Constraints are imposed on the total memory and on the average processing time per data item....
David Littau, Daniel Boley
CVPR
2010
IEEE
14 years 1 months ago
Local Features Are Not Lonely - Laplacian Sparse Coding for Image Classification
Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization pro...
Shenghua Gao, Wai-Hung Tsang, Liang-Tien Chia, Pei...