This paper studies the effect of covariance regularization for classification of high-dimensional data. This is done by fitting a mixture of Gaussians with a regularized covariance matrix to each class. Three data sets are chosen to suggest the results are applicable to any domain with high-dimensional data. The regularization needs of the data when pre-processed using the dimensionality reduct...

Traditionally, the strong uncorrelating transformation (SUT) is applied to the zero-lag sample autocovariance and pseudoautocovariance matrices of the observed mixtures for separating complexvalued stationary sources. The performance of the SUT in that context has been recently analyzed. In this work we extend the analysis to the case where the SUT is applied to “generalized” covariance and pse...

Gaussian mixture model (GMM) techniques are popular for speaker identification. Theoretically, each Gaussian function should have a full covariance matrix. However, the diagonal covariance matrix is usually used because the inverse of diagonal covariance matrix can be easily calculated via expectation maximization (EM) algorithm. This paper proposes a new probabilistic principal component analy...

In this report we describe our analysis of the influence of sea clutter non stationarity on the clutter covariance matrix estimation and its impact on the CFAR property of the normalized adaptive matched filter (NAMF). Three estimators have been considered in the analysis, i.e. the sample covariance matrix (SCM), the normalized sample covariance matrix (NSCM), and the fixed point (PF) estimator...