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EACL
2006
ACL Anthology
13 years 9 months ago
Improving Probabilistic Latent Semantic Analysis with Principal Component Analysis
Probabilistic Latent Semantic Analysis (PLSA) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis (LSA). However, th...
Ayman Farahat, Francine Chen
ICMCS
2005
IEEE
138views Multimedia» more  ICMCS 2005»
14 years 1 months ago
Overcomplete ICA-based Manmade Scene Classification
Principal Component Analysis (PCA) has been widely used to extract features for pattern recognition problems such as object recognition. Oliva and Torralba used “spatial envelop...
Matthew Boutell, Jiebo Luo
ECCV
2008
Springer
13 years 8 months ago
Discriminative Locality Alignment
—This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing...
Tianhao Zhang, Dacheng Tao, Jie Yang
TIT
2002
72views more  TIT 2002»
13 years 7 months ago
Principal curves with bounded turn
Principal curves, like principal components, are a tool used in multivariate analysis for ends like feature extraction. Defined in their original form, principal curves need not ex...
S. Sandilya, Sanjeev R. Kulkarni
WEBI
2005
Springer
14 years 1 months ago
HITS is Principal Components Analysis
In this work, we show that Kleinberg’s hubs and authorities model (HITS) is simply Principal Components Analysis (PCA; maybe the most widely used multivariate statistical analys...
Marco Saerens, François Fouss