Centrality and Diversity in Search by M. N. Murty & Anirban Biswas

Author:M. N. Murty & Anirban Biswas
Language: eng
Format: epub
ISBN: 9783030247133
Publisher: Springer International Publishing

where q is a fraction in (0, 1) and x(j) is the component of x, for . So, fractional norms reduce the diversity in classification by NNC and its variants.

5.Approximate NN: There are several schemes suggested to get an approximate NN of a test pattern x. One recent contribution in this direction is the locality sensitive hashing (LSH) which could be viewed simply as obtaining the NN in a randomly selected subset of features. It combines the approximate NNs obtained by some L random subsets of features. So, neighborhood is defined here based on a collection of neighbors in several random subspaces. This combination can reduce the diversity in NNC as it operates in lower dimensional subspaces.

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