502 search results - page 28 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
ICML
2008
IEEE
14 years 9 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...
MICCAI
2000
Springer
14 years 10 days 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...
ICPR
2010
IEEE
13 years 10 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 ...
SDM
2011
SIAM
12 years 11 months 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
11 years 11 months 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...