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NIPS
2003
13 years 9 months ago
Gene Expression Clustering with Functional Mixture Models
We propose a functional mixture model for simultaneous clustering and alignment of sets of curves measured on a discrete time grid. The model is specifically tailored to gene exp...
Darya Chudova, Christopher E. Hart, Eric Mjolsness...
ICML
2005
IEEE
14 years 8 months ago
Supervised dimensionality reduction using mixture models
Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We...
Sajama, Alon Orlitsky
ML
2000
ACM
157views Machine Learning» more  ML 2000»
13 years 7 months ago
A Multistrategy Approach to Classifier Learning from Time Series
We present an approach to inductive concept learning using multiple models for time series. Our objective is to improve the efficiency and accuracy of concept learning by decomposi...
William H. Hsu, Sylvian R. Ray, David C. Wilkins
CORR
2011
Springer
210views Education» more  CORR 2011»
13 years 2 months ago
Statistical Compressed Sensing of Gaussian Mixture Models
A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribu...
Guoshen Yu, Guillermo Sapiro
BMCBI
2006
203views more  BMCBI 2006»
13 years 7 months ago
Independent component analysis reveals new and biologically significant structures in micro array data
Background: An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem...
Attila Frigyesi, Srinivas Veerla, David Lindgren, ...