The subspace-based methods are effectively applied to classify sets of feature vectors by modeling them as subspaces. It is, however, difficult to appropriately determine the subspace dimensionality in advance for better performance. For alleviating such issue, we present a generalized mutual subspace method by introducing soft weighting across the basis vectors of the subspace. The bases are e...

We present a framework for Tensor-based subspace Tracking via Kronecker-structured projections (TeTraKron). TeTraKron allows to extend arbitrary matrix-based subspace tracking schemes to track the tensor-based subspace estimate. The latter can be computed via a structured projection applied to the matrix-based subspace estimate which enforces the multi-dimensional structure in a computationally...

A modification of the iterative matrix diagonalization method of Davidson is presented that is applicable to the symmetric eigenvalue problem. This method is based on subspace projections of a sequence of one or more approximate matrices. The purpose of these approximate matrices is to improve the efficiency of the solution of the desired eigenpairs by reducing the number of matrix-vector produ...

Numerical polynomial algebra emerges as a growing field of study in recent years with a broad spectrum of applications and many robust algorithms. Among the challenges in solving polynomial algebra problems with floating-point arithmetic, difficulties frequently arise in regularizing ill-posedness and handling large matrices. We elaborate regularization principles for reformulating the illposed...

Vector quantization(VQ) is an attractive technique for lossy data compression, which has been a key technology for data storage and/or transfer. So far, various competitive learning (CL) algorithms have been proposed to design optimal codebooks presenting quantization with minimized errors. However, their practical use has been limited for large scale problems, due to the computational complexi...

This paper presents a new method for text independent speaker recognition. We embed both training and test sessions into a session space. The session space is a direct sum of a common-speaker subspace and a speaker-unique subspace. The common-speaker subspace is Euclidean and is spanned by a set of reference sessions. Kernel-PCA is used to explicitly embed sessions into the common-speaker subsp...

Projection pursuit is concerned with finding interesting lowdimensional subspace of multivariate data. In this paper we proposed a genetic optimization approach to find the globally optimal orthogonal subspace given training data and user defined criterion on what subspaces are interesting. We then applied this approach to human face recognition. Suppose face recognition is done by simple corre...

This paper describes a color classification technique for the color subspaces definition based in 3D reconstruction approaches. These color subspaces use implicit functions to create a bounding surface that will fit a set of characteristic color samples to define a particular color. The implicit subspace reconstruction allow to define clusters of arbitrary shape for a better approximation of th...

In this article algebraic multigrid as preconditioners are designed, with biorthogonal wavelets, as intergrid operators for the Krylov subspace iterative methods. Construction of hierarchy of matrices in algebraic multigrid context is based on lowpass filter version of Wavelet Transform. The robustness and efficiency of this new approach is tested by applying it to large sparse, unsymmetric and...