High-throughput data dimension reduction via seeded canonical correlation analysis

2014 ◽  
Vol 29 (3) ◽  
pp. 193-199 ◽  
Author(s):  
Yunju Im ◽  
HeyIn Gang ◽  
Jae Keun Yoo
Keyword(s):  
Correlation Analysis ◽  
Dimension Reduction ◽  
Canonical Correlation Analysis ◽  
High Throughput ◽  
Canonical Correlation ◽  
High Throughput Data

scholarly journals Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction

2015 ◽  
Vol 27 (11) ◽  
pp. 3111-3124 ◽  
Author(s):  
Yong Luo ◽  
Dacheng Tao ◽  
Kotagiri Ramamohanarao ◽  
Chao Xu ◽  
Yonggang Wen
Keyword(s):  
Correlation Analysis ◽  
Dimension Reduction ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation

HIGHLIGHTING RELATIONSHIPS BETWEEN HETEROGENEOUS BIOLOGICAL DATA THROUGH GRAPHICAL DISPLAYS BASED ON REGULARIZED CANONICAL CORRELATION ANALYSIS

2009 ◽  
Vol 17 (02) ◽  
pp. 173-199 ◽  
Author(s):  
I. GONZÁLEZ ◽  
S. DÉJEAN ◽  
P. G. P. MARTIN ◽  
O. GONÇALVES ◽  
P. BESSE ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
High Throughput ◽  
Canonical Correlation ◽  
Cancer Cell Line ◽  
Biological Data ◽  
Data Sets ◽  
Gene Expressions ◽  
Cell Line Panel

Biological data produced by high throughput technologies are becoming more and more abundant and are arousing many statistical questions. This paper addresses one of them; when gene expression data are jointly observed with other variables with the purpose of highlighting significant relationships between gene expression and these other variables. One relevant statistical method to explore these relationships is Canonical Correlation Analysis (CCA). Unfortunately, in the context of postgenomic data, the number of variables (gene expressions) is usually greater than the number of units (samples) and CCA cannot be directly performed: a regularized version is required. We applied regularized CCA on data sets from two different studies and show that its interpretation evidences both previously validated relationships and new hypothesis. From the first data sets (nutrigenomic study), we generated interesting hypothesis on the transcription factor pathways potentially linking hepatic fatty acids and gene expression. From the second data sets (pharmacogenomic study on the NCI-60 cancer cell line panel), we identified new ABC transporter candidate substrates which relevancy is illustrated by the concomitant identification of several known substrates. In conclusion, the use of regularized CCA is likely to be relevant to a number and a variety of biological experiments involving the generation of high throughput data. We demonstrated here its ability to enhance the range of relevant conclusions that can be drawn from these relatively expensive experiments.

scholarly journals Analysis of correlation based dimension reduction methods

2011 ◽  
Vol 21 (3) ◽  
pp. 549-558 ◽  
Author(s):  
Yong Shin ◽  
Cheong Park
Keyword(s):  
Correlation Analysis ◽  
Dimension Reduction ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Orthogonal Transformation ◽  
Classification Performance ◽  
Class Label ◽  
Label Information ◽  
Reduction Methods

Analysis of correlation based dimension reduction methodsDimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised method. It has been shown that the classification performance of DCCA is superior to that of CCA due to the discriminative power using class label information. On the other hand, Linear Discriminant Analysis (LDA) is a supervised dimension reduction method and it is known as a special case of CCA. In this paper, we analyze the relationship between DCCA and LDA, showing that the projective directions by DCCA are equal to the ones obtained from LDA with respect to an orthogonal transformation. Using the relation with LDA, we propose a new method that can enhance the performance of DCCA. The experimental results show that the proposed method exhibits better classification performance than the original DCCA.

Analysis of Maximum Expiratory Flow Volume Curves Using Canonical Correlation Analysis

1985 ◽  
Vol 24 (02) ◽  
pp. 91-100 ◽  
Author(s):  
W. van Pelt ◽  
Ph. H. Quanjer ◽  
M. E. Wise ◽  
E. van der Burg ◽  
R. van der Lende
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Flow Volume ◽  
Non Linear ◽  
Maximum Expiratory Flow ◽  
Expiratory Flow ◽  
Relationship Of ◽  
The Relationship ◽  
Age And Sex

SummaryAs part of a population study on chronic lung disease in the Netherlands, an investigation is made of the relationship of both age and sex with indices describing the maximum expiratory flow-volume (MEFV) curve. To determine the relationship, non-linear canonical correlation was used as realized in the computer program CANALS, a combination of ordinary canonical correlation analysis (CCA) and non-linear transformations of the variables. This method enhances the generality of the relationship to be found and has the advantage of showing the relative importance of categories or ranges within a variable with respect to that relationship. The above is exemplified by describing the relationship of age and sex with variables concerning respiratory symptoms and smoking habits. The analysis of age and sex with MEFV curve indices shows that non-linear canonical correlation analysis is an efficient tool in analysing size and shape of the MEFV curve and can be used to derive parameters concerning the whole curve.

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