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» PCA in Autocorrelation Space
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CACM
2010
104views more  CACM 2010»
13 years 7 months ago
Faster dimension reduction
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pix...
Nir Ailon, Bernard Chazelle
ICIP
2007
IEEE
14 years 9 months ago
DNA Microarray Image Intensity Extraction using Eigenspots
DNA microarrays are commonly used in the rapid analysis of gene expression in organisms. Image analysis is used to measure the average intensity of circular image areas (spots), w...
Sotirios A. Tsaftaris, Ramandeep Ahuja, Derek J. S...
ICML
2004
IEEE
14 years 8 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
FGR
2006
IEEE
179views Biometrics» more  FGR 2006»
14 years 1 months ago
Articulated Hand Tracking by PCA-ICA Approach
—This paper introduces a new representation of hand motions for tracking and recognizing hand-finger gestures in an image sequence. A human hand has many joints, for example our ...
Makoto Kato, Yen-Wei Chen, Gang Xu
IEEEARES
2006
IEEE
14 years 1 months ago
Identifying Intrusions in Computer Networks with Principal Component Analysis
Most current anomaly Intrusion Detection Systems (IDSs) detect computer network behavior as normal or abnormal but cannot identify the type of attacks. Moreover, most current intr...
Wei Wang, Roberto Battiti