Code covered by the BSD License
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TCLANEIG(A,n,quality, Anorm, ...
TCLANEIG Compute factor space for specified approx. quality.
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compute_int(mu,j,delta,eta,LL...
COMPUTE_INT: Determine which Lanczos vectors to reorthogonalize against.
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lanpro(A,nin,kmax,r,options,....
LANPRO Lanczos tridiagonalization with partial reorthogonalization
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pythag(y,z)
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refinebounds(D,bnd,tol1)
REFINEBONDS Refines error bounds for Ritz values based on gap-structure
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reorth(Q,r,normr,index,alpha,...
REORTH Reorthogonalize a vector using iterated Gram-Schmidt
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tcdoc_getargs(pnames,dflts,va...
STATGETARGS Process parameter name/value pairs for statistics functions
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tcslsi(data, varargin)
TCSLSI Sequrntial algorithm of dimension reduction
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tqlb_matlab(alpha,beta)
TQLB: Compute eigenvalues and top and bottom elements of
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Sequential Latent Semantic Indexing
by Vital
27 Jan 2009
Sequential version of the latent semantic indexing method
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| File Information |
| Description |
Thus, this work offeres a sequential version of the LSI algorithm
(SLSI). Its main difference from the existing algorithms is that the
dimension of space is not fixed and dynamically changes to
ensure a given level of relative approximation error of a matrix of
observations. Experiments with a real text collections show that
the SLSI algorithm can be seen as a compromise, which has a
lower computational complexity and memory requirements
compared to the standard LSI method and does not lead to a
decrease of quality of classification in contrast to other sequential
algorithms. |
| MATLAB release |
MATLAB 7.0.1 (R14SP1)
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