of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes

We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all

Abstract not found

consists of shutting down resources during peri-ods of inactivity. In other words, one can adopt a dynamic power management (DPM) policy that dictates how and when various components should be shut down according to a system’s workload. Workload-driven DPM can be very effective, thanks to sophisticated

information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decompositions including channel

face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed. Index Terms—Facial image representation, local binary pattern, component-based face recognition, texture features

The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describing components that extract properties from unstructured objects

The purpose of this paper is to build the foundation for software architecture. We first develop an intuition for software architecture by appealing to several well-established architectural disciplines. On the basis of this intuition, we present a model of software architec-ture that consists

program source code. Bandera takes as input Java source code and generates a program model in the input language of one of several existing verification tools; Bandera also maps verifier outputs back to the original source code. We discuss the major components of Bandera and give an overview of how it can

ABSTRACT: The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a two-component