DISTRIBUTED SIMILARITY LEARNING FOR HIGH-DIMENSIONAL IMAGE FEATURES

摘要

A system and method for distributed similarity learning for high-dimensional image features are described. A set of data features is accessed. Subspaces from a space formed by the set of data features are determined using a set of projection matrices. Each subspace has a dimension lower than a dimension of the set of data features. Similarity functions are computed for the subspaces. Each similarity function is based on the dimension of the corresponding subspace. A linear combination of the similarity functions is performed to determine a similarity function for the set of data features.

主权项

1. A method comprising:
accessing a set of data features; determining a plurality of subspaces from a space formed by the set of data features using a set of projection matrices, each subspace having a dimension lower than a dimension of the set of data features; computing, using a processor of a machine, a plurality of similarity functions for the plurality of subspaces, each similarity function based on a dimension of a corresponding subspace; and performing a linear combination of the plurality of similarity functions to determine a similarity function for the set of data features.