METHOD, COMPUTER PROGRAM PRODUCT, AND DEVICE FOR CONDUCTING A MULTI-CRITERIA SIMILARITY SEARCH
First Claim
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1. A method for searching for similarities among multiple near-neighbor objects based on multiple criteria, comprising the steps of:
- receiving a query for an object closest to a query object;
assigning weights to distance functions among the multiple objects at the time of the query, each distance function representing a different criterion, wherein the weights are assigned by a user, the objects are indexed and represented as high-dimensional feature vectors, and each distance function is a metric on a subset of features;
finding a weight vector that is close to the object and retrieving a hash function corresponding to the weight vector, wherein the user assigned weights affect the selectivity of the features used in the hashing process, and the more weight a user specifies for a specific feature, the more likely that feature is to be selected in a hashing process.calculating the weighted average for the distance functions; and
determining the closest object to the query object within a given distance based on the weighted average for the distance functions and based on the hashing process using the retrieved hash function.
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Abstract
Similarities among multiple near-neighbor objects are searched for based on multiple criteria. A query is received for an object closest to an object provided by a user, and weights are assigned by a user to distance functions among the multiple objects at the time of the query. Each distance function represents a different criterion. The weighted average is calculated for the distance functions, and the closest object to the query object based on the weighted average for the distance functions.
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2 Claims
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1. A method for searching for similarities among multiple near-neighbor objects based on multiple criteria, comprising the steps of:
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receiving a query for an object closest to a query object; assigning weights to distance functions among the multiple objects at the time of the query, each distance function representing a different criterion, wherein the weights are assigned by a user, the objects are indexed and represented as high-dimensional feature vectors, and each distance function is a metric on a subset of features; finding a weight vector that is close to the object and retrieving a hash function corresponding to the weight vector, wherein the user assigned weights affect the selectivity of the features used in the hashing process, and the more weight a user specifies for a specific feature, the more likely that feature is to be selected in a hashing process. calculating the weighted average for the distance functions; and determining the closest object to the query object within a given distance based on the weighted average for the distance functions and based on the hashing process using the retrieved hash function.
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2-18. -18. (canceled)
Specification