CLUSTERING USING NON-NEGATIVE MATRIX FACTORIZATION ON SPARSE GRAPHS
First Claim
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1. A clustering method comprising:
- constructing a nonnegative sparse similarity matrix for a set of objects;
performing nonnegative factorization of the nonnegative sparse similarity matrix; and
allocating objects of the set of objects to clusters based on factor matrices generated by the nonnegative factorization of the nonnegative sparse similarity matrix.
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Abstract
Object clustering techniques are disclosed. A nonnegative sparse similarity matrix is constructed for a set of objects. Nonnegative factorization of the nonnegative sparse similarity matrix is performed. Objects of the set of objects are allocated to clusters based on factor matrices generated by the nonnegative factorization of the nonnegative sparse similarity matrix.
102 Citations
26 Claims
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1. A clustering method comprising:
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constructing a nonnegative sparse similarity matrix for a set of objects; performing nonnegative factorization of the nonnegative sparse similarity matrix; and allocating objects of the set of objects to clusters based on factor matrices generated by the nonnegative factorization of the nonnegative sparse similarity matrix. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A clustering system comprising:
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a processor configured to; construct a nonnegative sparse similarity matrix for a set of objects, perform nonnegative factorization of the nonnegative sparse similarity matrix, and allocate objects of the set of objects to clusters based on factor matrices generated by the nonnegative factorization of the nonnegative sparse similarity matrix; and a user interface configured to display a representation of the cluster allocations of objects of the set of objects. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26)
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Specification