scholarly journals A New Robust Deep Canonical Correlation Analysis Algorithm for Small Sample Problems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 33631-33639
Author(s):  
Yan Liu ◽  
Yun Li ◽  
Yun-Hao Yuan ◽  
Hui Zhang
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Small Sample ◽  
Analysis Algorithm

Real-time damage detection of degrading systems

2019 ◽  
Vol 19 (3) ◽  
pp. 810-837 ◽  
Author(s):  
Tapas Tripura ◽  
Basuraj Bhowmik ◽  
Vikram Pakrashi ◽  
Budhaditya Hazra
Keyword(s):  
Correlation Analysis ◽  
Damage Detection ◽  
Real Time ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Degree Of Freedom ◽  
Time Varying ◽  
Analysis Algorithm ◽  
Stiffness Reduction ◽  
The Impact

In this article, a robust output-only real-time damage detection technique for multi-degree-of-freedom degrading systems using recursive canonical correlation analysis is presented. It has been observed that the impact of damage to a vibrating system gradually advances with time that sustains until the system degrades up to a considerable extent. Of significant interest is the effect of sudden damage in presence of continuous degradation in real-time, which is studied in the form of a sudden stiffness reduction in a separate floor. The proposed recursive canonical correlation analysis algorithm estimates the iterative update of eigenspace at each instant from the response data, thereby capturing the features of a time varying degrading structure in an online framework. Furthermore, recursive canonical correlation analysis algorithm is shown to reduce the computational cost by updating the eigenspace at each instant of time. This article explores newly developed recursive condition indicators: recursive Mahalanobis distance and recursive Itakura distance that elicit damage information from the eigenspace. In order to model degradation, simulations aimed at successfully capturing the behavior of the process in real-time becomes imperative. A general stochastic formulation of the coupled response-degradation problem accounting for the evolution of degradation is presented in the light of stiffness degradation problems. The evolution of time varying system responses is generated using a newly proposed Ito–Taylor expansion-based stochastic numerical integration formulation. Numerically simulated structural vibrating systems, namely, 2-degree-of-freedom base-isolated and 4-degree-of-freedom linear systems, have been used to check the performance of the recursive canonical correlation analysis method. The spatial damage detectability of the algorithm in real-time is explored through identifying crack location on a beam traversed by a vehicle. Finally, an experimental case study has been carried out to verify the robustness of the proposed algorithm. The identification results for both numerical and experimental cases demonstrate the efficacy of the proposed algorithm in identification of nonlinear and time varying behavior associated with degrading structural systems.

Study of Canonical Correlation Analysis Algorithm Based on Kernel Function

2013 ◽  
Vol 791-793 ◽  
pp. 1191-1194
Author(s):  
Qiong Guan ◽  
Jian Bin Fang
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Kernel Function ◽  
Computer Technology ◽  
Canonical Correlation ◽  
Rapid Development ◽  
Nonlinear Relationship ◽  
Analysis Algorithm ◽  
Practical Applications ◽  
Software Technology

With the rapid development of computer technology and software technology, the application of linear canonical correlation analysis is more and more widely. But in practical applications, variables are often potential nonlinear relations. Therefore, it is necessary to study the nonlinear canonical correlation analysis algorithm, reveal the nonlinear relationship between variables of potential.

scholarly journals Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies

PLoS Genetics ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. e1008973
Author(s):  
Helian Feng ◽  
Nicholas Mancuso ◽  
Alexander Gusev ◽  
Arunabha Majumdar ◽  
Megan Major ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Association Studies ◽  
Expression Patterns ◽  
Small Sample ◽  
Type I ◽  
Sparse Canonical Correlation Analysis ◽  
Eqtl Data

Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expression quantitative trait locus (eQTL) data used to train the genetic predictors have small sample sizes, or when data from causally relevant tissues are not available. Here, we propose to address these issues by integrating multiple tissues in the TWAS using sparse canonical correlation analysis (sCCA). We show that sCCA-TWAS combined with single-tissue TWAS using an aggregate Cauchy association test (ACAT) outperforms traditional single-tissue TWAS. In empirically motivated simulations, the sCCA+ACAT approach yielded the highest power to detect a gene associated with phenotype, even when expression in the causal tissue was not directly measured, while controlling the Type I error when there is no association between gene expression and phenotype. For example, when gene expression explains 2% of the variability in outcome, and the GWAS sample size is 20,000, the average power difference between the ACAT combined test of sCCA features and single-tissue, versus single-tissue combined with Generalized Berk-Jones (GBJ) method, single-tissue combined with S-MultiXcan, UTMOST, or summarizing cross-tissue expression patterns using Principal Component Analysis (PCA) approaches was 5%, 8%, 5% and 38%, respectively. The gain in power is likely due to sCCA cross-tissue features being more likely to be detectably heritable. When applied to publicly available summary statistics from 10 complex traits, the sCCA+ACAT test was able to increase the number of testable genes and identify on average an additional 400 additional gene-trait associations that single-trait TWAS missed. Our results suggest that aggregating eQTL data across multiple tissues using sCCA can improve the sensitivity of TWAS while controlling for the false positive rate.

scholarly journals Underwater Acoustic Target Feature Fusion Method Based on Multi-Kernel Sparsity Preserve Multi-Set Canonical Correlation Analysis

2019 ◽  
Vol 37 (1) ◽  
pp. 87-92
Author(s):  
Honghui Yang ◽  
Shuzhen Yi
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Target Recognition ◽  
Small Sample Size ◽  
Small Sample ◽  
Projection Algorithm ◽  
Fusion Method ◽  
Underwater Target

To solve high-dimensional and small-sample-size classification problem for underwater target recognition, a new feature fusion method is proposed based on multi-kernel sparsity preserve multi-set canonical correlation analysis. The multi-set canonical correlation analysis algorithm is used to quantitatively analyze the correlation of multi-domain features, remove redundant and noise features, in order to achieve multi-domain feature fusion. The multi-kernel sparsely preserved projection algorithm is used to constrain the sparse reconstruction of the extracted multi-domain feature samples, which enhances the feature's classification ability. Results of applying real radiated noise datasets to underwater target recognition experiments show that our new method can effectively remove the redundancy and noise features, achieve the fusion of multi-domain underwater target features, and improve the recognition accuracy of underwater targets.

Sign in / Sign up

Export Citation Format

Share Document