Nonlinear structure of escape-times to falls for a passive dynamic walker on an irregular slope: Anomaly detection using multi-class support vector machine and latent state extraction by canonical correlation analysis

2011 ◽  
Author(s):  
H. Suetani ◽  
A. M. Ideta ◽  
J. Morimoto
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Anomaly Detection ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Support Vector ◽  
Nonlinear Structure ◽  
Passive Dynamic Walker

scholarly journals Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

2021 ◽  
Vol 11 (23) ◽  
pp. 11453
Author(s):  
Yuhang Gao ◽  
Juanning Si ◽  
Sijin Wu ◽  
Weixian Li ◽  
Hao Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Steady State ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Classification Accuracy ◽  
Canonical Correlation ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Support Vector ◽  
Visual Evoked

Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific information. Moreover, over-fitting may occur when a short time window (TW) length was used in CCA. In this article, an optimized L1-regularized multiway canonical correlation analysis (L1-MCCA) is combined with a support vector machine (SVM) to overcome the aforementioned shortcomings in CCA. The correlation coefficients obtained by L1-MCCA were transferred into a particle-swarm-optimization (PSO)-optimized support vector machine (SVM) classifier to improve the classification accuracy. The performance of the proposed method was evaluated and compared with the traditional CCA and power spectral density (PSD) methods. The results showed that the accuracy of the L1-MCCA-PSO-SVM was 96.36% and 98.18% respectively when the TW lengths were 2 s and 6 s. This accuracy is higher than that of the traditional CCA and PSD methods.

scholarly journals Prediction of MicroRNA and Gene Target in Synovium-Associated Pain of Knee Osteoarthritis Based on Canonical Correlation Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Haiming Wang ◽  
Yue Hu ◽  
Yujie Xie ◽  
Li Wang ◽  
Jianxiong Wang ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Knee Osteoarthritis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Rank Correlation ◽  
Mendelian Inheritance ◽  
Functional Enrichment ◽  
Support Vector ◽  
Gene Target ◽  
Knee Oa

Inflammation plays a central role in knee osteoarthritis (OA) pathogenesis (C. R. Scanzello, 2017). The synovial membrane inflammation is associated with disease progression and represents a primary source of agony in knee OA (L. A. Stoppiello et al., 2014). Many inflammatory mediators may have biomarker utility. To identify synovium related to knee OA pain biomarkers, we used canonical correlation analysis to analyze the miRNA-mRNA dual expression profiling data and extracted the miRNAs and mRNAs. After identifying miRNAs and mRNAs, we built an interaction network by integrating miRWalk2.0. Then, we extended the network by increasing miRNA-mRNA pairs and identified five miRNAs and four genes (TGFBR2, DST, TBXAS1, and FHLI) through the Spearman rank correlation test. For miRNAs involved in the network, we further performed the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, whereafter only those mRNAs overlapped with the Online Mendelian Inheritance in Man (OMIM) genetic database were analyzed. Receiver operating characteristic (ROC) curve and support vector machine (SVM) classification were taken into the analysis. The results demonstrated that all the recognized miRNAs and their gene targets in the network might be potential biomarkers for synovial-associated pain in knee OA. This study predicts the underlying risk biomarkers of synovium pain in knee OA.

Estimation of the mechanical properties of steel via LIBS combined with canonical correlation analysis (CCA) and support vector regression (SVR)

2018 ◽  
Vol 33 (5) ◽  
pp. 720-729 ◽  
Author(s):  
Jianwei Huang ◽  
Meirong Dong ◽  
Shengzi Lu ◽  
Wenbing Li ◽  
Jidong Lu ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Correlation Analysis ◽  
Support Vector Regression ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Support Vector

The mechanical properties of steel with different aging degree microstructure was estimated by combining LIBS and chemometrics (CCA and SVR).

Higher-Order Regularized Kernel Canonical Correlation Analysis

2015 ◽  
Vol 29 (04) ◽  
pp. 1551005 ◽  
Author(s):  
Md. Ashad Alam ◽  
Kenji Fukumizu
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Kernel Methods ◽  
Canonical Correlation ◽  
Higher Order ◽  
Support Vector ◽  
Real World Data ◽  
Gaussian Kernels ◽  
Canonical Variables ◽  
Kernel Canonical Correlation Analysis

It is well known that the performance of kernel methods depends on the choice of appropriate kernels and associated parameters. While cross-validation (CV) is a useful method of kernel and parameter choice for supervised learning such as the support vector machines, there are no general well-founded methods for unsupervised kernel methods. This paper discusses CV for kernel canonical correlation analysis (KCCA), and proposes a new regularization approach for KCCA. As we demonstrate with Gaussian kernels, the CV errors for KCCA tend to decrease as the bandwidth parameter of the kernel decreases, which provides inappropriate features with all the data concentrated in a few points. This is caused by the ill-posedness of the KCCA with the CV. To solve this problem, we propose to use constraints on the fourth-order moments of canonical variables in addition to the variances. Experiments on synthesized and real-world data demonstrate that the proposed higher-order regularized KCCA can be applied effectively with the CV to find appropriate kernel and regularization parameters.

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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