Parallel Computing for Dimensionality Reduction

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

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Methods and Technologies of Parallel Computing for Mathematical Modeling of Stress-Strain State of Constructions Taking into Account Ductile Fracture

Journal of Automation and Information Sciences ◽

10.1615/jautomatinfscien.v46.i11.30 ◽

2014 ◽

Vol 46 (11) ◽

pp. 23-35 ◽

Cited By ~ 6

Author(s):

Elena A. Velikoivanenko ◽

Alexey S. Milenin ◽

Alexander V. Popov ◽

Vladimir A. Sidoruk ◽

Alexander N. Khimich

Keyword(s):

Mathematical Modeling ◽

Parallel Computing ◽

Ductile Fracture ◽

Strain State ◽

Stress Strain ◽

Stress Strain State

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Speech Emotion Recognition Based on Sparse Representation

Archives of Acoustics ◽

10.2478/aoa-2013-0055 ◽

2013 ◽

Vol 38 (4) ◽

pp. 465-470 ◽

Cited By ~ 11

Author(s):

Jingjie Yan ◽

Xiaolan Wang ◽

Weiyi Gu ◽

LiLi Ma

Keyword(s):

Dimensionality Reduction ◽

Emotion Recognition ◽

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Speech Emotion Recognition ◽

Least Squares Regression ◽

Computer Science Pedagogy ◽

Reduction Methods ◽

Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

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Matlab and Parallel Computing

Image Processing & Communications ◽

10.2478/v10248-012-0048-5 ◽

2012 ◽

Vol 17 (4) ◽

pp. 207-216 ◽

Cited By ~ 5

Author(s):

Magdalena Szymczyk ◽

Piotr Szymczyk

Keyword(s):

Image Processing ◽

Signal Processing ◽

Parallel Computing ◽

Distributed Computing ◽

Control Systems ◽

High Performance ◽

Parallel Applications ◽

Process Simulations ◽

Key Features ◽

Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

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