Two decades of research shows that Principle Component Analysis is effective and convenient for representation and recognition of human face images. It is a kind of subspace method. Many successful face recognition algorithms follow the subspace method and try to find better subspaces for face recognition. In this paper, we present the projection incorporated subspace method based on PCA. This ...

Let S be a locally compact foundation semigroup with identity and                          be its semigroup algebra. Let X be a weak*-closed left translation invariant subspace of    In this paper, we prove that  X  is invariantly  complemented in   if and  only if  the left ideal  of    has a bounded approximate identity. We also prove that a foundation semigroup with identity S is left amenab...

In this paper, we propose a texture representation framework to map local texture patches into a low-dimensional texture subspace. In natural texture images, textons are entangled with multiple factors, such as rotation, scaling, viewpoint variation, illumination change, and non-rigid surface deformation. Mapping local texture patches into a low-dimensional subspace can alleviate or eliminate t...

We report the results of a comparative study on subspace analysis methods for face recognition. In particular, we have studied four different subspace representations and their ‘kernelized’ versions if available. They include both unsupervised methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), and supervised methods such as Fisher Discriminant Analysis ...

In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plus-anomalous matrix decomposition aided by random subspace techniques and subsequently detect traffic anomalies in the anomalous subspace using a statistical test. Experimental r...

The block preconditioned steepest descent iteration is an iterative eigensolver for subspace eigenvalue and eigenvector computations. An important area of application of the method is the approximate solution of mesh eigenproblems for self-adjoint and elliptic partial differential operators. The subspace iteration allows to compute some of the smallest eigenvalues together with the associated i...

Dimensionality reduction and segmentation have been used as methods to reduce the complexity of the representation of hyperspectral remote sensing images. In this study, a new object-oriented mapping approach is proposed based on nonlinear subspace feature analysis of hyperspectral remote sensing images. Nonlinear local manifold learning approaches for feature extraction were utilized to obtain...

Nuisance attribute projection (NAP) and within-class covariance normalization (WCCN) are two effective techniques for intersession variability compensation in SVM based speaker verification systems. However, by normalizing or removing the nuisance subspace containing the session variability can not guarantee to enlarge the distance between speakers. In this paper, we investigated the probabilit...

The building blocks introduced earlier by us in [1] are used for constructing a hierarchical nonlinear model for nonlinear factor analysis. We call the resulting method hierarchical nonlinear factor analysis (HNFA). The variational Bayesian learning algorithm used in this method has a linear computational complexity, and it is able to infer the structure of the model in addition to estimating t...

This paper demonstrates of Prior Subspace Analysis (PSA) as a method for transcribing drums in the presence of pitched instruments. PSA uses prior subspaces that represent the sources to be transcribed to overcome some of the problems associated with other subspace methods such as Independent Subspace Analysis (ISA) or sub-band ISA. The use of prior knowledge results in improved robustness for ...