Paper Info
Title
Immediate Reward Reinforcement Learning for Projective Kernel Methods
Abstract
We extend a reinforcement learning algorithm which has previously been shown to cluster data. We have previously applied the method to unsupervised projection methods, principal component analy- sis, exploratory projection pursuit and canonical correlation analysis. We now show how the same methods can be used in feature spaces to per- form kernel principal component analysis and kernel canonical correlation analysis.
Year
Venue
Keywords
2007
ESANN
reinforcement learning,kernel principal component analysis,kernel method,feature space,projection pursuit,principal component,projection method,canonical correlation analysis
Field
DocType
Citations 
Pattern recognition,Radial basis function kernel,Projection pursuit,Computer science,Kernel embedding of distributions,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Kernel method,Variable kernel density estimation,Machine learning,Kernel (statistics)
Conference
2
PageRank 
References 
Authors
0.40
7
2
Name
Order
Citations
PageRank
Colin Fyfe150855.62
Pei Ling Lai29218.78