Central Subspace Dimensionality Reduction Using Covariance Operators

2011 ◽  
Vol 33 (4) ◽  
pp. 657-670 ◽  
Author(s):  
Minyoung Kim ◽  
V Pavlovic
Keyword(s):  
Dimensionality Reduction ◽  
Covariance Operators ◽  
Central Subspace

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

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.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
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.

Nonlinear Dimensionality Reduction for Data on Manifold with Rings

2009 ◽  
Vol 19 (11) ◽  
pp. 2908-2920
Author(s):  
De-Yu MENG ◽  
Nan-Nan GU ◽  
Zong-Ben XU ◽  
Yee LEUNG
Keyword(s):  
Dimensionality Reduction ◽  
Nonlinear Dimensionality Reduction

СONTRIBUTION OF POLYMORPHIC VARIATION OF ТP53 AND XRCC1 GENES TO THE SUSCEPTIBILITY TO BREAST CANCER FOR WOMEN OF KYRGYZ AND BELARUSIAN NATIONALITY - A COMPARATIVE STUDY ON MULTIFACTOR DIMENSIONALITY REDUCTION METHODS

2018 ◽  
Vol 64 (1) ◽  
pp. 95-101
Author(s):  
Nazira Aldasheva ◽  
Vyacheslav Kipen ◽  
Zhaynagul Isakova ◽  
Sergey Melnov ◽  
Raisa Smolyakova ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Rflp Analysis ◽  
Kyrgyz Republic ◽  
Increased Risk ◽  
Polymorphic Variants ◽  
The Republic

Basing on Multifactor Dimensionality Reduction method we showed that polymorphic variants p.Q399R (rs25487, XRCC1) and p.P72R (rs1042522, TP53) correlated with increased risk of breast cancer for women from the Kyrgyz Republic and the Republic of Belarus. Cohort for investigation included patients with clinically verified breast cancer: 117 women from the Kyrgyz Republic (nationality - Kyrgyz) and 169 - of the Republic of Belarus (nationality - Belarusians). Group for comparison included (healthy patients without history of cancer pathology at the time of blood sampling) 102 patients from the Kyrgyz Republic, 185 - from the Republic of Belarus. Respectively genotyping of polymorphic variants p.Q399R (rs25487, XRCC1) and p.P72R (rs1042522, TP53) was done by PCR-RFLP. Analysis of the intergenic interactions conducted with MDR 3.0.2 software. Both ethnic groups showed an increase of breast cancer risk in the presence of alleles for SNPs Gln p.Q399R (XRCC1) in the heterozygous state: for the group “Kyrgyz” - OR=2,78 (95% CI=[1,60-4,82]), p=0,001; for the group “Belarusians” - OR=1,85 (95% СІ=[1Д1-2,82], p=0,004. Carriers with combination of alleles Gln (p.Q399R, XRCC1) and Pro (p.P72R, TP53) showed statistically significance increases of breast cancer risk as for patients from the Kyrgyz Republic (OR=2,89, 95% CI=[1,33-6,31]), so as for patients from the Republic of Belarus (OR=3,01, 95% CI=[0,79-11,56]).

Sparse Embedding: A Framework for Sparsity Promoting Dimensionality Reduction

2012 ◽  
pp. 414-427 ◽  
Author(s):  
Hien V. Nguyen ◽  
Vishal M. Patel ◽  
Nasser M. Nasrabadi ◽  
Rama Chellappa
Keyword(s):  
Dimensionality Reduction

Unsupervised dimensionality reduction for very large datasets: Are we going to the right direction?

2020 ◽  
Vol 196 ◽  
pp. 105777
Author(s):  
Jadson Jose Monteiro Oliveira ◽  
Robson Leonardo Ferreira Cordeiro
Keyword(s):  
Dimensionality Reduction ◽  
Large Datasets ◽  
Very Large Datasets ◽  
The Right
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