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==Introduction== | ==Introduction== | ||
This paper tries to propose a method to learn mappings from high dimensional data to binary codes. One of the main advantages of using binary space is one can do exact KNN classification in sublinear time. Like other metric learning method this paper also tries to optimize some cost function which is based one a similarity measure between data points. | This paper tries to propose a method to learn mappings from high dimensional data to binary codes. One of the main advantages of using binary space is one can do exact KNN classification in sublinear time. Like other metric learning method this paper also tries to optimize some cost function which is based one a similarity measure between data points. One choice of similarity measure in binary space is Euclidean distance which produces unsatisfactory results. Another choice is Hamming distance, which is the total number of positions at which the corresponding symbols are different. | ||
Revision as of 00:11, 7 July 2013
Introduction
This paper tries to propose a method to learn mappings from high dimensional data to binary codes. One of the main advantages of using binary space is one can do exact KNN classification in sublinear time. Like other metric learning method this paper also tries to optimize some cost function which is based one a similarity measure between data points. One choice of similarity measure in binary space is Euclidean distance which produces unsatisfactory results. Another choice is Hamming distance, which is the total number of positions at which the corresponding symbols are different.