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A constrained non-negative matrix factorization in information retrieval | IEEE Conference Publication | IEEE Xplore

A constrained non-negative matrix factorization in information retrieval


Abstract:

A novel method, which is called constrained non-negative matrix factorization, is presented to capture the latent semantic relations. An objective function is defined to ...Show More

Abstract:

A novel method, which is called constrained non-negative matrix factorization, is presented to capture the latent semantic relations. An objective function is defined to impose three additional constraints, in addition to the non-negativity constraint in the standard non-negative matrix factorization. The update rules to solve the objective function with these constraints are presented, and its convergence is proved. In contrast to the standard non-negative matrix factorization, the constrained non-negative matrix factorization can capture the semantic relations as orthogonal as possible. The experiments indicate that the constrained non-negative matrix factorization has better precision than the standard non-negative matrix factorization in information retrieval.
Date of Conference: 27-29 October 2003
Date Added to IEEE Xplore: 08 January 2004
Print ISBN:0-7803-8242-0
Conference Location: Las Vegas, NV, USA

References

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