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» Gene set analysis using principal components
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CVPR
2005
IEEE
16 years 5 months ago
Online Detection and Classification of Moving Objects Using Progressively Improving Detectors
Boosting based detection methods have successfully been used for robust detection of faces and pedestrians. However, a very large amount of labeled examples are required for train...
Omar Javed, Saad Ali, Mubarak Shah
117
Voted
MVA
2002
116views Computer Vision» more  MVA 2002»
15 years 3 months ago
Contour Extraction in Medical Images Using B-Snake Model
In this paper, a close-form B-snake model is presented for contour extraction in medical images. Based on our previous research work [lo], the Principal Component Analysis (PCA) h...
Yue Wang, Eam Khwang Teoh
137
Voted
NECO
1998
151views more  NECO 1998»
15 years 3 months ago
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
120
Voted
BMCBI
2006
119views more  BMCBI 2006»
15 years 3 months ago
Utilization of two sample t-test statistics from redundant probe sets to evaluate different probe set algorithms in GeneChip stu
Background: The choice of probe set algorithms for expression summary in a GeneChip study has a great impact on subsequent gene expression data analysis. Spiked-in cRNAs with know...
Zihua Hu, Gail R. Willsky
NIPS
2000
15 years 4 months ago
Automatic Choice of Dimensionality for PCA
A central issue in principal component analysis (PCA) is choosing the number of principal components to be retained. By interpreting PCA as density estimation, this paper shows ho...
Thomas P. Minka