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COMPLIFE
2006
Springer
13 years 10 months ago
Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models
We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. The approach is based on the combination of hidden Markov model...
Illia Horenko, Johannes Schmidt-Ehrenberg, Christo...
BMCBI
2008
115views more  BMCBI 2008»
13 years 6 months ago
Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data
Background: Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differ...
Sudhakar Jonnalagadda, Rajagopalan Srinivasan
JMLR
2010
155views more  JMLR 2010»
13 years 1 months ago
Structured Sparse Principal Component Analysis
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespeci...
Rodolphe Jenatton, Guillaume Obozinski, Francis Ba...
ADBIS
2003
Springer
108views Database» more  ADBIS 2003»
13 years 12 months ago
Dynamic Integration of Classifiers in the Space of Principal Components
Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble...
Alexey Tsymbal, Mykola Pechenizkiy, Seppo Puuronen...
EDCC
2005
Springer
14 years 7 days ago
Formal Safety Analysis of a Radio-Based Railroad Crossing Using Deductive Cause-Consequence Analysis (DCCA)
Abstract. In this paper we present the formal safety analysis of a radiobased railroad crossing. We use deductive cause-consequence analysis (DCCA) as analysis method. DCCA is a no...
Frank Ortmeier, Wolfgang Reif, Gerhard Schellhorn