scholarly journals A New Feature Extraction Algorithm Based on Orthogonal Regularized Kernel CCA and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Xinchen Guo ◽  
Xiuling Fan ◽  
Xiantian Xi ◽  
Fugeng Zeng
Keyword(s):  
Canonical Correlation ◽  
Kernel Method ◽  
Random Variables ◽  
Nonlinear Problems ◽  
Analysis Algorithm ◽  
Linear Relationships ◽  
Extraction Algorithm ◽  
Kernel Canonical Correlation Analysis ◽  
New Feature ◽  
Kernel Cca

In this paper, an orthogonal regularized kernel canonical correlation analysis algorithm (ORKCCA) is proposed. ORCCA algorithm can deal with the linear relationships between two groups of random variables. But if the linear relationships between two groups of random variables do not exist, the performance of ORCCA algorithm will not work well. Linear orthogonal regularized CCA algorithm is extended to nonlinear space by introducing the kernel method into CCA. Simulation experimental results on both artificial and handwritten numerals databases show that the proposed method outperforms ORCCA for the nonlinear problems.

Robust kernel canonical correlation analysis to detect gene-gene co-associations: A case study in genetics

2019 ◽  
Vol 17 (04) ◽  
pp. 1950028 ◽  
Author(s):  
Md. Ashad Alam ◽  
Osamu Komori ◽  
Hong-Wen Deng ◽  
Vince D. Calhoun ◽  
Yu-Ping Wang
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Enrichment Analysis ◽  
Superior Performance ◽  
Covariance Operator ◽  
Contaminated Data ◽  
Cross Covariance ◽  
Kernel Canonical Correlation Analysis ◽  
Kernel Cca

The kernel canonical correlation analysis based U-statistic (KCCU) is being used to detect nonlinear gene–gene co-associations. Estimating the variance of the KCCU is however computationally intensive. In addition, the kernel canonical correlation analysis (kernel CCA) is not robust to contaminated data. Using a robust kernel mean element and a robust kernel (cross)-covariance operator potentially enables the use of a robust kernel CCA, which is studied in this paper. We first propose an influence function-based estimator for the variance of the KCCU. We then present a non-parametric robust KCCU, which is designed for dealing with contaminated data. The robust KCCU is less sensitive to noise than KCCU. We investigate the proposed method using both synthesized and real data from the Mind Clinical Imaging Consortium (MCIC). We show through simulation studies that the power of the proposed methods is a monotonically increasing function of sample size, and the robust test statistics bring incremental gains in power. To demonstrate the advantage of the robust kernel CCA, we study MCIC data among 22,442 candidate Schizophrenia genes for gene–gene co-associations. We select 768 genes with strong evidence for shedding light on gene–gene interaction networks for Schizophrenia. By performing gene ontology enrichment analysis, pathway analysis, gene–gene network and other studies, the proposed robust methods can find undiscovered genes in addition to significant gene pairs, and demonstrate superior performance over several of current approaches.

scholarly journals Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Dabin Jeong ◽  
Sangsoo Lim ◽  
Sangseon Lee ◽  
Minsik Oh ◽  
Changyun Cho ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Computational Method ◽  
Transcriptional Networks ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Data Set ◽  
Kernel Canonical Correlation Analysis ◽  
Kernel Cca

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.

scholarly journals KERNEL MAD ALGORITHM FOR RELATIVE RADIOMETRIC NORMALIZATION

2016 ◽  
Vol III-1 ◽  
pp. 49-53 ◽  
Author(s):  
Yang Bai ◽  
Ping Tang ◽  
Changmiao Hu
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Landsat 8 ◽  
Linear Relationships ◽  
Multi Temporal ◽  
Kernel Canonical Correlation Analysis ◽  
The Difference ◽  
Complex Relationships ◽  
Beijing China

The multivariate alteration detection (MAD) algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA) which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA). The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1) data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization.

scholarly journals KERNEL MAD ALGORITHM FOR RELATIVE RADIOMETRIC NORMALIZATION

2016 ◽  
Vol III-1 ◽  
pp. 49-53
Author(s):  
Yang Bai ◽  
Ping Tang ◽  
Changmiao Hu
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Landsat 8 ◽  
Linear Relationships ◽  
Multi Temporal ◽  
Kernel Canonical Correlation Analysis ◽  
The Difference ◽  
Complex Relationships ◽  
Beijing China

The multivariate alteration detection (MAD) algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA) which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA). The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1) data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization.

An operator approach to analysis of conditional kernel canonical correlation

2015 ◽  
Vol 13 (04) ◽  
pp. 1550024 ◽  
Author(s):  
Jia Cai
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation ◽  
General Setting ◽  
Rates Of Convergence ◽  
Covariance Operator ◽  
Operator Approach ◽  
Learning Rates ◽  
Cross Covariance ◽  
Kernel Canonical Correlation Analysis ◽  
Kernel Cca

Kernel canonical correlation analysis (CCA) is a nonlinear extension of CCA, which aims at extracting information shared by two random variables. In this paper, a new notion of conditional kernel CCA is introduced. Conditional kernel CCA aims at analyzing the effect of variable Z to the dependence between X and Y. Rates of convergence of an empirical normalized conditional cross-covariance operator (empirical NCCCO) to the normalized conditional cross-covariance operator (NCCCO) are also investigated in this paper. Elaborate error analysis of conditional kernel CCA is elegantly conducted under mild decay conditions. Our refined analysis leads to satisfactory learning rates in a more general setting.

Kernel Feature Extraction Approach for Color Image Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1621-1626
Author(s):  
Xiao Yuan Jing ◽  
Kun Li ◽  
Song Song Wu ◽  
Yong Fang Yao ◽  
Chao Wang
Keyword(s):  
Correlation Analysis ◽  
Image Recognition ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Color Image ◽  
Kernel Method ◽  
Recognition Performance ◽  
Image Databases ◽  
Color Recognition ◽  
Kernel Canonical Correlation Analysis

Color Image Recognition is one of the most important fields in Pattern Recognition. Both Multi-set canonical correlation analysis and Kernel method are important techniques in the field of color image recognition. In this paper, we combine the two methods and propose one novel color image recognition approach: color image kernel canonical correlation analysis (CIKCCA). Color image kernel canonical correlation analysis is based on the theory of multi-set canonical correlation analysis and extracts canonical correlation features among the color image components. Then fuse the features of the color image components in the feature level, which are used for classification and recognition. Experimental results on the FRGC-v2 public color image databases demonstrate that the proposed approach acquire better recognition performance than other color recognition methods.

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