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 ...
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...
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...
Leonardo R. Bachega, Guangzhi Cao, Charles A. Boum...
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...
The integrated approach is a classifier established on statistical estimator and artificial neural network. This consists of preliminary data whitening transformation which provide...