kernelized Locality-Sensitive Hashing: Difference between revisions
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Locality Sensitive Hashing (LSH) is a form of dimension reduction that finds embedding of high dimensional data into a low dimensional hamming space while providing probabilistic collision guarantees. That is, similar data points will have the same low dimensional mapping with high probability. One immediate application of LSH is large scale nearest neighbour search. A large database of objects (e.g. images) can be partitioned into disjoint buckets so that objects of the a single bucket share the same low dimensional representation which is used as a key to that bucket. At query time, the low dimensional representation of the query object determines a single bucket of “the most probably similar objects” which can then be searched in the traditional way. | Locality Sensitive Hashing (LSH) is a form of dimension reduction that finds embedding of high dimensional data into a low dimensional hamming space while providing probabilistic collision guarantees. That is, similar data points will have the same low dimensional mapping with high probability. One immediate application of LSH is large scale nearest neighbour search/classification. A large database of objects (e.g. images) can be partitioned into disjoint buckets so that objects of the a single bucket share the same low dimensional representation which is used as a key to that bucket. At query time, the low dimensional representation of the query object determines a single bucket of “the most probably similar objects” which can then be searched in the traditional way. | ||
For each similarity measure, a locality sensitive hashing method has to be designed carefully to ensure practically acceptable probabilistic collision guarantees. It was also noticed that most of the previous work done on LSH assumes that data points come from multidimensional vector space and the underlying embedding is explicitly known. However, that is not always the case. For example the RBF kernel maps the data to an infinite dimensional space which is intractable to explicitly work with. This paper generalizes locality sensitive hashing by proposing a fully kernelized method that provides the desirable collision guarantees. | For each similarity measure, a locality sensitive hashing method has to be designed carefully to ensure practically acceptable probabilistic collision guarantees. It was also noticed that most of the previous work done on LSH assumes that data points come from multidimensional vector space and the underlying embedding is explicitly known. However, that is not always the case. For example the RBF kernel maps the data to an infinite dimensional space which is intractable to explicitly work with. This paper generalizes locality sensitive hashing by proposing a fully kernelized method that provides the desirable collision guarantees. | ||
Revision as of 14:01, 7 July 2013
Locality Sensitive Hashing (LSH) is a form of dimension reduction that finds embedding of high dimensional data into a low dimensional hamming space while providing probabilistic collision guarantees. That is, similar data points will have the same low dimensional mapping with high probability. One immediate application of LSH is large scale nearest neighbour search/classification. A large database of objects (e.g. images) can be partitioned into disjoint buckets so that objects of the a single bucket share the same low dimensional representation which is used as a key to that bucket. At query time, the low dimensional representation of the query object determines a single bucket of “the most probably similar objects” which can then be searched in the traditional way.
For each similarity measure, a locality sensitive hashing method has to be designed carefully to ensure practically acceptable probabilistic collision guarantees. It was also noticed that most of the previous work done on LSH assumes that data points come from multidimensional vector space and the underlying embedding is explicitly known. However, that is not always the case. For example the RBF kernel maps the data to an infinite dimensional space which is intractable to explicitly work with. This paper generalizes locality sensitive hashing by proposing a fully kernelized method that provides the desirable collision guarantees.