Journée Mathematical Foundations of Learning Theory
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|Statistical Analysis of Subspace Methods and Associated Learning Algorithms|
John Shawe-Taylor (University of Southampton)
2 juin 2006
Subspace inference is a critical component in many practical applications of learning from data, yet very little analysis has been made of the performance of these algorithms. The talk considers the question of providing a statistical analysis of subspace methods and of learning using the associated representations. We begin with considering principal components analysis and the relation between process and empirical eigenvalues. We go on to consider more advanced techniques such as canonical correlation analysis and linear functions learned in the inferred representation. Sparse analogies of these techniques will be discussed with associated bounds.