In this paper we describe a holistic face recognition method based on subspace Linear Dis-criminant Analysis (LDA). The method consists of two steps: rst we project the face image from the original vector space to a face subspace via Principal Component Analysis where the subspace dimension is carefully chosen, and then we use LDA to obtain a linear classiier in the subspace. The criterion we u...

Subspace clustering tries to find groups of similar objects from the given dataset such that the objects are projected on only a subset of the feature space. It finds meaningful clusters in all possible subspaces. However, when it comes to the quality of the resultant subspace clusters most of the subspace clusters are redundant. These redundant subspace clusters don’t provide new information. ...

this study develops and analyzes preconditioned krylov subspace methods to solve linear systemsarising from discretization of the time-independent space-fractional models. first, we apply shifted grunwald formulas to obtain a stable finite difference approximation to fractional advection-diffusion equations. then, we employee two preconditioned iterative methods, namely, the preconditioned gene...

Independent subspace anlaysis (ISA) is a linear modelbased method which generalizes independent component analysis (ICA) by incorporating the invariant feature subspace into multidimensional ICA. In this paper we apply ISA to the problem of gene expression data analysis and show the useful behavior of the independent subspaces of gene expression data in the task of gene clustering and gene-gene...

This article presents a new subspace-based technique for reducing the noise of signals in time-series. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is u...