We propose new algorithms for singular value decomposition (SVD) of very large-scale matrices based on a low-rank tensor approximation technique called the tensor train (TT) format. The proposed algorithms can compute several dominant singular values and corresponding singular vectors for large-scale structured matrices given in a TT format. The computational complexity of the proposed methods ...

In this paper, the performance of SVD and Schur decomposition is evaluated and compared for image copyright protection applications. The watermark image is embedded in the cover image by using Quantization Index Modulus Modulation (QIMM) and Quantization Index Modulation (QIM). Watermark image is embedded in the D matrix of Schur decomposition and Singular Value Decomposition (SVD). Watermarkin...

Spectral embedding based on the Singular Value Decomposition (SVD) is a widely used “preprocessing” step in many learning tasks, typically leading to dimensionality reduction by projecting onto a number of dominant singular vectors and rescaling the coordinate axes (by a predefined function of the singular value). However, the number of such vectors required to capture problem structure grows w...

Singular Value Decomposition (SVD) is considered to be the holy grail of matrix factorizations. Here an SVD method is used to classify handwritten digits and to aid in the recovery of marred facial images from an ensemble of similar images.

Aiming at rolling bearing fault diagnosis, the collected vibration signal contains complex noise interference, and one-dimensional information cannot be used to fully mine data features of problem. This paper proposes a diagnosis method based on SVD-GST combined with Vision Transformer. Firstly, is preprocessed reduce using singular value decomposition (SVD) obtain more accurate useful signal. ...

In this paper, experimental responses of the clamped mild steel, copper, and aluminium circular plates are presented subjected to blast loading. The GMDH-type neural networks (Group Method of Data Handling) are then used for the modelling of the mid-point deflection thickness ratio of the circular plates using those experimental results. The aim of such modelling is to show how the mid-point de...

In this study, a spatiotemporal estimation method based on Funk singular value decomposition (F-SVD) that considers the correlation of rainfall is proposed to improve estimations from gauge observations. Hourly data several flood events are selected verify by comparing with Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) in Hanjiang basin, China. The results show (1) F-SVD has best p...

Subcortical small vessel disease (SVD) is characterized by white matter damage resulting from arteriolosclerosis and chronic hypoperfusion. Transforming growth factor beta 1 (TGFB1) is dysregulated in the hereditary SVD, CARASIL (cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy). However, very little is known about the role of the largest group in the ...

BACKGROUND Stentless aortic bioprostheses were designed to provide enhanced hemodynamic performance and potentially greater longevity. The present report describes the outcomes of patients with the Freestyle stentless bioprosthesis followed for ≤18 years. METHODS AND RESULTS Between 1993 and 2011, 430 patients underwent primary aortic valve replacement with a Freestyle bioprosthesis in the su...