Regression and classification methods based on similarity of the input to                  stored examples have not been widely used in applications involving very large sets                  of high-dimensional data. Recent advances in computational geometry and machine                  learning, however, may alleviate the problems in using these methods on large data                  sets. This volume presents theoretical and practical discussions of nearest-neighbor                  (NN) methods in machine learning and examines computer vision as an application                  domain in which the benefit of these advanced methods is often dramatic. It brings                  together contributions from researchers in theory of computation, machine learning,                  and computer vision with the goals of bridging the gaps between disciplines and                  presenting state-of-the-art methods for emerging applications.The contributors focus                  on the importance of designing algorithms for NN search, and for the related                  classification, regression, and retrieval tasks, that remain efficient even as the                  number of points or the dimensionality of the data grows very large. The book begins                  with two theoretical chapters on computational geometry and then explores ways to                  make the NN approach practicable in machine learning applications where the                  dimensionality of the data and the size of the data sets make the naïve methods for                  NN search prohibitively expensive. The final chapters describe successful                  applications of an NN algorithm, locality-sensitive hashing (LSH), to vision                  tasks.

Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing) PDF

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