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» A Practical Method for the Sparse Resultant
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ICCAD
2006
IEEE
141views Hardware» more  ICCAD 2006»
15 years 8 months ago
An accurate sparse matrix based framework for statistical static timing analysis
Statistical Static Timing Analysis has received wide attention recently and emerged as a viable technique for manufacturability analysis. To be useful, however, it is important th...
Anand Ramalingam, Gi-Joon Nam, Ashish Kumar Singh,...
SDM
2011
SIAM
370views Data Mining» more  SDM 2011»
14 years 2 months ago
Sparse Latent Semantic Analysis
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The k...
Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G....
CVPR
2001
IEEE
16 years 1 months ago
Bayesian Learning of Sparse Classifiers
Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant...
Anil K. Jain, Mário A. T. Figueiredo
ICA
2007
Springer
15 years 3 months ago
Estimating the Mixing Matrix in Sparse Component Analysis Based on Converting a Multiple Dominant to a Single Dominant Problem
We propose a new method for estimating the mixing matrix, A, in the linear model x(t) = As(t), t = 1, . . . , T, for the problem of underdetermined Sparse Component Analysis (SCA)....
Nima Noorshams, Massoud Babaie-Zadeh, Christian Ju...
ECCV
2000
Springer
16 years 1 months ago
Learning to Recognize 3D Objects with SNoW
This paper describes a novel view-based learning algorithm for 3D object recognition from 2D images using a network of linear units. The SNoW learning architecture is a sparse netw...
Ming-Hsuan Yang, Dan Roth, Narendra Ahuja