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BMCBI
2010
144views more  BMCBI 2010»
13 years 7 months ago
Super-sparse principal component analyses for high-throughput genomic data
Background: Principal component analysis (PCA) has gained popularity as a method for the analysis of highdimensional genomic data. However, it is often difficult to interpret the ...
Donghwan Lee, Woojoo Lee, Youngjo Lee, Yudi Pawita...
IROS
2006
IEEE
148views Robotics» more  IROS 2006»
14 years 1 months ago
Environment Understanding: Robust Feature Extraction from Range Sensor Data
— This paper proposes an approach allowing indoor environment supervised learning to recognize relevant features for environment understanding. Stochastic preprocessing methods i...
Antonio Romeo, Luis Montano
CNSR
2011
IEEE
257views Communications» more  CNSR 2011»
12 years 11 months ago
On Threshold Selection for Principal Component Based Network Anomaly Detection
—Principal component based anomaly detection has emerged as an important statistical tool for network anomaly detection. It works by projecting summary network information onto a...
Petar Djukic, Biswajit Nandy
NIPS
1993
13 years 9 months ago
Fast Pruning Using Principal Components
We present a new algorithm for eliminating excess parameters and improving network generalization after supervised training. The method, \Principal Components Pruning (PCP)",...
Asriel U. Levin, Todd K. Leen, John E. Moody
ICIP
2008
IEEE
14 years 9 months ago
Robust video fingerprinting based on 2D-OPCA of affine covariant regions
This paper proposes a robust video fingerprinting method based on 2-Dimensional Oriented Principal Component Analysis (2D-OPCA) of affine covariant regions. The goal of video fing...
Chang Dong Yoo, Sunil Lee