502 search results - page 28 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
ICML
2008
IEEE
15 years 10 months ago
2008
IEEE
We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the vector: a cardinality constraint limits the number of...
125
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MICCAI
2000
Springer
15 years 1 months ago
2000
Springer
We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medic...
99
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ICPR
2010
IEEE
14 years 11 months ago
2010
IEEE
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in ...
163
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SDM
2011
SIAM
14 years 14 days ago
2011
SIAM
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...
JMLR
2012
13 years 3 days ago
2012
We study sparse principal components analysis in the high-dimensional setting, where p (the number of variables) can be much larger than n (the number of observations). We prove o...