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NPL
2006
100views more  NPL 2006»
13 years 10 months ago
Constrained Projection Approximation Algorithms for Principal Component Analysis
Abstract. In this paper we introduce a new error measure, integrated reconstruction error (IRE) and show that the minimization of IRE leads to principal eigenvectors (without rotat...
Seungjin Choi, Jong-Hoon Ahn, Andrzej Cichocki
BMCBI
2008
115views more  BMCBI 2008»
13 years 10 months ago
Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data
Background: Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differ...
Sudhakar Jonnalagadda, Rajagopalan Srinivasan
PERSUASIVE
2009
Springer
14 years 4 months ago
Communication-based influence components model
This paper discusses problems faced by planners of real-world online behavioural change interventions who must select behavioural change frameworks from a variety of competing the...
Brian Cugelman, Mike Thelwall, Phil Dawes
ICPR
2008
IEEE
14 years 11 months ago
Robust tracking of spatial related components
This paper introduces a hierarchical approach for multicomponent tracking, where the object-to-be-tracked is modeled as a group of spatial related parts. We propose to use a robus...
Horst Bischof, Michael Donoser, Thomas Mauthner
ISCAS
2008
IEEE
169views Hardware» more  ISCAS 2008»
14 years 4 months ago
Sigma-delta learning for super-resolution independent component analysis
— Many source separation algorithms fail to deliver robust performance in presence of artifacts introduced by cross-channel redundancy, non-homogeneous mixing and highdimensional...
Amin Fazel, Shantanu Chakrabartty