The method of truncated total least squares [2] is an alternative to the classical truncated singular value decomposition used for the regularization of ill-conditioned linear systems Ax ≈ b [3]. Truncation methods aim at limiting the contribution of noise or rounding errors by cutting off a certain number of terms in an expansion such as the singular value decomposition. To this end a truncati...

We consider the improvement in accuracy of latent semantic analysis when a part of speech tagger is used to augment a term/document matrix. We first construct an augmented term/document matrix as input into singular value decomposition (SVD). The singular values then serve as principal components for a cosine projection. The results show that the addition of POS tags can decrease ambiguities si...

n this paper, we apply picard’s iteration method followed by adomian decomposition method to solve a nonlinear singular cauchy problem of euler- poisson- darboux equation. the solution of the problem is much simplified and shorter to arriving at the solution as compared to the technique applied by carroll and showalter (1976)in the solution of singular cauchy problem.

Singular value decomposition and information theoretic criterion based clutter reduction is proposed for ground penetrating radar imaging. The scheme is capable of discriminating target, clutter and noise subspaces. Information theoretic criterion is used with conventional singular value decomposition to find the target singular values. The proposed scheme also works for extracting multiple tar...

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abstract In certain signal processing applications it is required to compute the null space of a matrix whose rows are samples of a signal with p components. The usual tool for doing this is the singular value decomposition. However, the singular value decomposition has the drawback that it requires O(p 3) operations to recompute when a new sample arrives. In this paper, we show that a diierent...

1 OPERATOR DECOMPOSITIONS In this lecture we will consider two basic decompositions of operators that will be used repeatedly in the course: the spectral decomposition and the singular-value decomposition. The spectral decomposition holds only for normal operators, while the singular-value decomposition holds for all operators. The singular-value decomposition will be particularly important in ...

The authors propose a new hybrid watermarking scheme for copyright protection of color images using contourlet transform and singular value decomposition. The host color image and color watermark images are decomposed into directional sub-bands using contourlet transform and then applied Singular value decomposition to mid frequency sub-band

Latent semantic indexing (LSI) is an application of numerical method called singular value decomposition (SVD), which discovers latent semantic in documents by creating concepts from existing terms. The application area is not limited to text retrieval, many applications such as image compression are known. We propose usage of SVD as a possible data mining method and lattice size reduction tool...