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APPT
2005
Springer
14 years 1 months ago
Principal Component Analysis for Distributed Data Sets with Updating
Identifying the patterns of large data sets is a key requirement in data mining. A powerful technique for this purpose is the principal component analysis (PCA). PCA-based clusteri...
Zheng-Jian Bai, Raymond H. Chan, Franklin T. Luk
EWMF
2005
Springer
14 years 1 months ago
Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis
Abstract. We show that eigenvector decomposition can be used to extract a term taxonomy from a given collection of text documents. So far, methods based on eigenvector decompositio...
Holger Bast, Georges Dupret, Debapriyo Majumdar, B...
SGAI
2005
Springer
14 years 1 months ago
The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data
This paper presents the results of an investigation into the use of machine learning methods for the identification of narcotics from Raman spectra. The classification of spectr...
Tom Howley, Michael G. Madden, Marie-Louise O'Conn...
CORR
2010
Springer
160views Education» more  CORR 2010»
13 years 7 months ago
PCA 4 DCA: The Application Of Principal Component Analysis To The Dendritic Cell Algorithm
As one of the newest members in the field of artificial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). U...
Feng Gu, Julie Greensmith, Robert Oates, Uwe Aicke...
PR
2006
116views more  PR 2006»
13 years 7 months ago
Correspondence matching using kernel principal components analysis and label consistency constraints
This paper investigates spectral approaches to the problem of point pattern matching. We make two contributions. First, we consider rigid point-set alignment. Here we show how ker...
Hongfang Wang, Edwin R. Hancock