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» Applying Discrete PCA in Data Analysis
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ACCV
2006
Springer
14 years 1 months ago
Fast Image Replacement Using Multi-resolution Approach
We developed a system including two modules: the texture analysis module and the texture synthesis module. The analysis module is capable of analyzing an input image and performing...
Chih-Wei Fang, James Jenn-Jier Lien
INTERSPEECH
2010
13 years 2 months ago
Detecting categorical perception in continuous discrimination data
We present a method for assessing categorical perception from continuous discrimination data. Until recently, categorical perception of speech has exclusively been measured by dis...
Paul Boersma, Katerina Chládková
NIPS
2004
13 years 8 months ago
Two-Dimensional Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional d...
Jieping Ye, Ravi Janardan, Qi Li
BMCBI
2005
110views more  BMCBI 2005»
13 years 7 months ago
Considerations when using the significance analysis of microarrays (SAM) algorithm
Background: Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments...
Ola Larsson, Claes Wahlestedt, James A. Timmons
BMCBI
2008
95views more  BMCBI 2008»
13 years 7 months ago
Unsupervised reduction of random noise in complex data by a row-specific, sorted principal component-guided method
Background: Large biological data sets, such as expression profiles, benefit from reduction of random noise. Principal component (PC) analysis has been used for this purpose, but ...
Joseph W. Foley, Fumiaki Katagiri