37 search results - page 1 / 8 » Covariance Estimation for High Dimensional Data Vectors Usin... |
NIPS
2008
13 years 6 months ago
2008
Covariance estimation for high dimensional vectors is a classically difficult problem in statistical analysis and machine learning. In this paper, we propose a maximum likelihood ...
JMLR
2010
12 years 11 months ago
2010
This paper considers the problem of estimating a high dimensional inverse covariance matrix that can be well approximated by "sparse" matrices. Taking advantage of the c...
ICASSP
2010
IEEE
13 years 4 months ago
2010
IEEE
Recently, the Sparse Matrix Transform (SMT) has been proposed as a tool for estimating the eigen-decomposition of high dimensional data vectors [1]. The SMT approach has two major...
IDEAL
2003
Springer
13 years 9 months ago
2003
Springer
To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with Fuzzy clustering. The prop...
SSPR
2004
Springer
13 years 10 months ago
2004
Springer
The integrated approach is a classifier established on statistical estimator and artificial neural network. This consists of preliminary data whitening transformation which provide...