Ranking of documents in a very large database
First Claim
1. A method for retrieving and/or ranking documents in a database, said method comprising the steps of:
- providing a document matrix from said documents, said matrix including numerical elements derived from said attribute data;
providing covariance matrix from said document matrix;
computing eigenvectors of said covariance matrix using neural network algorithm(s);
computing inner products of said eigenvectors to create the said sum S and examining convergence of said sum S such that difference between the sums becomes not more than a predetermined threshold to determine the final set of said eigenvectors;
providing said set of eigenvectors to the singular value decomposition of said covariance matrix so as to obtain the following formula;
K=V·
Σ
·
VT, wherein K represents said covariance matrix, V represents the matrix consisting of eigenvectors, Σ
represents a diagonal matrix, and VT represents the transpose of the matrix V;
reducing the dimension of said matrix V using predetermined numbers of eigenvectors included in said matrix V, said eigenvectors including an eigenvector corresponding to the largest singular value; and
reducing the dimension of said document matrix using said dimension reduced matrix V.
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Abstract
The present invention discloses a method, a computer system, a program product which provide a useful interface to rank the documents in a very large database using neural network(s). The method comprising the steps of: providing a document matrix from said documents, said matrix including numerical elements derived from said attribute data; providing the covariance matrix from said document matrix; computing the eigenvectors of said covariance matrix using neural network algorithm(s); computing inner products of said eigenvectors to create sum S
and examining convergence of said sum S such that difference between the sums becomes not more than a predetermined threshold to determine a final set of said eigenvectors; providing said set of eigenvectors to the singular value decom position of said covariance matrix.
31 Citations
14 Claims
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1. A method for retrieving and/or ranking documents in a database, said method comprising the steps of:
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providing a document matrix from said documents, said matrix including numerical elements derived from said attribute data;
providing covariance matrix from said document matrix;
computing eigenvectors of said covariance matrix using neural network algorithm(s);
computing inner products of said eigenvectors to create the said sum S and examining convergence of said sum S such that difference between the sums becomes not more than a predetermined threshold to determine the final set of said eigenvectors;
providing said set of eigenvectors to the singular value decomposition of said covariance matrix so as to obtain the following formula;
K=V·
Σ
·
VT,wherein K represents said covariance matrix, V represents the matrix consisting of eigenvectors, Σ
represents a diagonal matrix, and VT represents the transpose of the matrix V;
reducing the dimension of said matrix V using predetermined numbers of eigenvectors included in said matrix V, said eigenvectors including an eigenvector corresponding to the largest singular value; and
reducing the dimension of said document matrix using said dimension reduced matrix V. - View Dependent Claims (2, 3, 4)
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5. A computer system for executing a method for retrieving and/or ranking documents in a database comprising:
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means for providing a document matrix from said documents, said matrix including numerical elements derived from said attribute data;
means for providing the covariance matrix from said document matrix;
means for computing eigenvectors of said covariance matrix using neural network algorithm(s);
means for computing inner products of said eigenvectors to create sum S and examining convergence of said sum S such that difference between the sums becomes not more than a predetermined threshold to determine a final set of said eigenvectors;
means for providing said set of eigenvectors to the singular value decomposition of said covariance matrix so as to obtain the following formula;
K=V·
Σ
·
VT,wherein K represents said covariance matrix, V represents the matrix consisting of eigenvectors, Σ
represents a diagonal matrix, and VT represents the transpose of the matrix V;
means for reducing the dimension of said matrix V using predetermined numbers of eigenvectors included in said matrix V, said eigenvectors including an eigenvector corresponding to the largest singular value; and
means for reducing the dimension of said document matrix using said dimension reduced matrix V. - View Dependent Claims (6, 7, 8)
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9. A program product including a computer readable computer program for executing a method for retrieving and/or ranking documents in a database, said method comprising the steps of;
- providing a document matrix from said documents, said matrix including numerical elements derived from said attribute data;
providing the covariance matrix from said document matrix;
computing eigenvectors of said covariance matrix using neural network algorithm(s);
computing inner products of said eigenvectors to create the said sum S and examining the convergence of said sum S such that the difference between the sums becomes not more than a predetermined threshold to determine a final set of said eigenvectors;
providing said set of eigenvectors to the singular value decomposition of said covariance matrix so as to obtain the following formula;
K=V·
Σ
·
VT,wherein K represents said covariance matrix, V represents the matrix consisting of eigenvectors, Σ
represents a diagonal matrix, and VT represents the transpose of the matrix V;
reducing the dimension of said matrix V using predetermined numbers of eigenvectors included in said matrix V, said eigenvectors including a eigenvector corresponding to the largest singular value; and
reducing the dimension of said document matrix using said dimension reduced matrix V. - View Dependent Claims (10, 12)
- providing a document matrix from said documents, said matrix including numerical elements derived from said attribute data;
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11. The program product according to the claim 19, wherein said covariance matrix is computed by the following formula;
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K=B−
Xbar·
XbarT,wherein K represents a covariance matrix in said covariance matrix, B represents a momentum matrix, Xbar represents a mean vector and XbarT represents a transpose thereof.
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13. A computer system comprising:
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means for providing a matrix from including numerical elements;
providing covariance matrix from said matrix;
means for computing eigenvectors of said covariance matrix using neural network algorithm(s);
means for computing inner products of said eigenvectors to create the said sum S and examining convergence of said sum S such that difference between the sums becomes not more than a predetermined threshold to determine a final set of said eigenvectors;
means for providing said set of eigenvectors to the singular value decomposition of said covariance matrix so as to obtain the following formula;
K=V·
Σ
·
VT,wherein K represents said covariance matrix, V represents the matrix consisting of eigenvectors, Σ
represents a diagonal matrix, and VT represents a transpose of the matrix V;
means for reducing the dimension of said matrix V using predetermined numbers of eigenvectors included in said matrix V, said eigenvectors including an eigenvector corresponding to the largest singular value; and
means for reducing the dimension of said matrix using said dimension reduced matrix V. - View Dependent Claims (14)
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Specification