with rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. because o...

disguised face recognition is a major challenge in the field of face recognition which has been taken less attention. therefore, in this paper a disguised face recognition algorithm based on local phase quantization (lpq) method and singular value decomposition (svd) is presented which deals with two main challenges. the first challenge is when an individual intentionally alters the appearance ...

the speech enhancement techniques are often employed to improve the quality and intelligibility of the noisy speech signals. this paper discusses a novel technique for speech enhancement which is based on singular value decomposition. this implementation utilizes a genetic algorithm based optimization method for reducing the effects of environmental noises from the singular vectors as well as t...

In designing various security and privacy related data mining applications, privacy preserving has become a major concern. Protecting sensitive or confidential information in data mining is an important long term goal. An increased data disclosure risks may encounter when it is released. Various data distortion techniques are widely used to protect sensitive data; these approaches protect data ...

The different orthogonal relationship that exists in the Löwdin orthogonalizat ions is presented. Other orthogonalizat ion techniques such as polar decomposition (PD), principal component analysis (PCA) and reduced singular value decomposition (SVD) can be derived from Löwdin methods. It is analytically shown that the polar decomposition is presented in the symmetric o rthogonalization; princip...

A Symmetry Preserving Singular Value Decomposition

Singular value decomposition (SVD) is a general-purpose mathematical analysis tool that has been used in a variety of information-retrieval applications. As the size and complexity of retrieval collections increase, it is crucial for our analysis tools to scale accordingly. To this end, we have studied the application of a new theoretically justiied SVD approximation algorithm to the problem of...

A powerful method for solving planar eigenvalue problems is the Method of Particular Solutions (MPS), which is also well known under the name “point matching method”. The implementation of this method usually depends on the solution of one of three types of linear algebra problems: singular value decomposition, generalized eigenvalue decomposition, or generalized singular value decomposition. W...