10 search results - page 2 / 2 » Full regularization path for sparse principal component anal... |
CIKM
2010
Springer
13 years 3 months ago
2010
Springer
Low-dimensional topic models have been proven very useful for modeling a large corpus of documents that share a relatively small number of topics. Dimensionality reduction tools s...
SDM
2010
SIAM
13 years 3 months ago
2010
SIAM
A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the o...
AAAI
2008
13 years 7 months ago
2008
Recent study has shown that canonical algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) can be obtained from graph based dimensionality ...
ICDM
2007
IEEE
13 years 8 months ago
2007
IEEE
Recently the problem of dimensionality reduction (or, subspace learning) has received a lot of interests in many fields of information processing, including data mining, informati...
APWEB
2005
Springer
13 years 10 months ago
2005
Springer
Association Rule Mining algorithms operate on a data matrix to derive association rule, discarding the quantities of the items, which contains valuable information. In order to mak...