The Random Projection Method

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Description

Random projection is a simple geometric technique for reducing the dimensionality of a set of points in Euclidean space while preserving pairwise distances approximately. The technique plays a key role in several breakthrough developments in the field of algorithms. In other cases, it provides elegant alternative proofs. The book begins with an elementary description of the technique and its basic properties. Then it develops the method in the context of applications, which are divided into three groups. The first group consists of combinatorial optimization problems such as maxcut, graph coloring, minimum multicut, graph bandwidth and VLSI layout.Presented in this context is the theory of Euclidean embeddings of graphs. The next group is machine learning problems, specifically, learning intersections of halfspaces and learning large margin hypotheses. The projection method is further refined for the latter application. The last set consists of problems inspired by information retrieval, namely, nearest neighbor search, geometric clustering and efficient low-rank approximation. Motivated by the first two applications, an extension of random projection to the hypercube is developed here. Throughout the book, random projection is used as a way to understand, simplify and connect progress on these important and seemingly unrelated problems. The book is suitable for graduate students and research mathematicians interested in computational geometry.
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Product details

  • Paperback | 105 pages
  • 171.45 x 247.65 x 6.35mm | 233g
  • Providence, United States
  • English
  • 0821837931
  • 9780821837931
  • 1,839,375

Table of contents

Random projection Combinatorial optimization: Rounding via random projection Embedding metrics in Euclidean space Euclidean embeddings: Beyond distance preservation Learning theory: Robust concepts Intersections of half-spaces Information retrieval: Nearest neighbors Indexing and clustering Bibliography Appendix.
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Review quote

"The book offers a broad view of its subject, with a good selection of examples and a vast set of bibliographic references. It could be used well as a starting point for research in this area. The presence of a number of exercises [also] makes it a possible choice for [a] textbook on this method."
-- Mathematical Reviews
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