Dimension Reduction: A Guided Tour (Foundations and Trends (R) in Machine Learning): 7 (Foundations and Trends® in Machine Learning)

Burges, Christopher J.C.

ISBN 10: 1601983786 / ISBN 13: 9781601983787

Published by now publishers Inc, 2010

About this Item

Like new and unread paperback - minor wear.

Seller Inventory # 9999-99988709420

Bibliographic Details

• Title: Dimension Reduction: A Guided Tour (Foundations and Trends (R) in Machine Learning): 7 (Foundations and Trends® in Machine Learning)
• Publisher: now publishers Inc
• Publication Date: 2010
• Language: English
• ISBN 10: 1601983786
• ISBN 13: 9781601983787
• Binding: Soft cover
• Condition: As New

About this title

Synopsis

We give a tutorial overview of several foundational methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the data lies. For projective methods, we review projection pursuit, principal component analysis (PCA), kernel PCA, probabilistic PCA, canonical correlation analysis (CCA), kernel CCA, Fisher discriminant analysis, oriented PCA, and several techniques for sufficient dimension reduction. For the manifold methods, we review multidimensional scaling (MDS), landmark MDS, Isomap, locally linear embedding, Laplacian eigenmaps, and spectral clustering. Although the review focuses on foundations, we also provide pointers to some more modern techniques. We also describe the correlation dimension as one method for estimating the intrinsic dimension, and we point out that the notion of dimension can be a scale-dependent quantity. The Nyström method, which links several of the manifold algorithms, is also reviewed. We use a publicly available dataset to illustrate some of the methods. The goal is to provide a self-contained overview of key concepts underlying many of these algorithms, and to give pointers for further reading.

About the Author

Burges is Distinguished Member of Technical Staff at Lucent Technologies, Bell Laboratories.