This paper proposes a neural network approach based on Error Back Propagation (EBP) for classification of different eye images. To reduce the complexity of layered neural network the dimensions of input vectors are optimized using Singular Value Decomposition (SVD). The main objective of this work is to prove usefulness of SVD to form a compact set of features for classification by EBP algorith...

Social Tagging has become a method of choice for enriching data (like pictures) with meta-data which in turn can be used for searching (like retrieving art pictures) or tag recommendations relying on Singular Value Decompositions (SVD) to reduce dimensionality. We observed that social tagging-based search or tag-recommendation is more successful, if several dimensions describing the tagging com...

The Matrix Factorization models, sometimes called the latent factor models, are a family of methods in the recommender system research area to (1) generate the latent factors for the users and the items and (2) predict users’ ratings on items based on their latent factors. However, current Matrix Factorization models presume that all the latent factors are equally weighted, which may not always...

OBJECTIVE To determine the prevalence of apathy and depression in cerebral small vessel disease (SVD), and the relationships between both apathy and depression with cognition. To examine whether apathy is specifically related to impairment in executive functioning and processing speed. METHODS 196 patients with a clinical lacunar stroke and an anatomically corresponding lacunar infarct on MRI...

BACKGROUND AND PURPOSE Endothelial dysfunction has been implicated in the pathogenesis of cerebral small-vessel disease (SVD). Endothelin (ET), released by the endothelium, plays a crucial role in vasoconstriction in the cerebral circulation and could contribute to the pathogenesis of cerebral SVD. Circulating ET levels may not reflect vascular production of endothelin-1 (ET-1), most of which i...

In this paper we present the usage of singular value decomposition (SVD) in text summarization. Firstly, we mention the taxonomy of generic text summarization methods. Then we describe principles of the SVD and its possibilities to identify semantically important parts of a text. We propose a modification of the SVD-based summarization, which improves the quality of generated extracts. In the s...

In this paper, we extend the well known QR-updating scheme to a similar but more versatile and generally applicable scheme for updating the singular value decomposition (SVD). This is done by supplementing the QR-updating with a Jacobi-type SVD procedure, where apparently only a few SVD steps after each QR-update su ce in order to restore an acceptable approximation for the SVD. This then resul...

We explore the use of the singular value decomposition (SVD) in image compression. We link the SVD and the multiresolution algorithms. In [22] it is derived a multiresolution representation of the SVD decomposition, and in [15] the SVD algorithm and Wavelets are linked, proposing a mixed algorithm which roughly consist on applying firstly a discrete Wavelet transform and secondly the SVD algori...

Variational Mode Decomposition (VMD) is widely used for inspection purposes. The initial parameters are usually set manually, which a limitation of this technique. In paper, method to automatically select these through combination Singular Value (SVD) and Improved-VMD (IVMD) proposed. VMD applied multiple times with varying K-value parameter. original signal its sub-signals arising from decompo...