scholarly journals Epilepsy EEG classification method based on supervised locality preserving canonical correlation analysis

2021 ◽  
Vol 19 (1) ◽  
pp. 624-642
Author(s):  
Hongming Liu ◽  
◽  
Yunyuan Gao ◽  
Jianhai Zhang ◽  
Juanjuan Zhang ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Binary Classification ◽  
Response Speed ◽  
Classification Method ◽  
High Dimensional ◽  
Data Set ◽  
Locality Preserving ◽  
Fusion Features

<abstract><p>Existing epileptic seizure automatic detection systems are often troubled by high-dimensional electroencephalogram (EEG) features. High-dimensional features will not only bring redundant information and noise, but also reduce the response speed of the system. In order to solve this problem, supervised locality preserving canonical correlation analysis (SLPCCA), which can effectively use both sample category information and nonlinear relationships between features, is introduced. And an epileptic signal classification method based on SLPCCA is proposed. Firstly, the power spectral density and the fluctuation index of the frequency slice wavelet transform are extracted as features from the EEG fragments. Next, SLPCCA obtains the optimal projection direction by maximizing the weight correlation between the paired samples in the class and their neighbors. And the projection combination of original features in the optimal direction is the fusion feature. The fusion features are then input into LS-SVM for training and testing. This method is verified on the Bonn dataset and the CHB-MIT dataset and gets good results. On various classification tasks of Bonn data set, the proposed method achieves an average classification accuracy of 99.16%. On the binary classification task of the inter-seizure and seizure epileptic EEG of the CHB-MIT dataset, the proposed method achieves an average accuracy of 97.18%. The experimental results show that the algorithm achieves excellent results compared with several state-of-the-art methods. In addition, the parameter sensitivity of SLPCCA and the relationship between the dimension of the fusion features and the classification results are discussed. Therefore, the stability and effectiveness of the method are further verified.</p></abstract>

scholarly journals Fault Detection of Diesel Engine Air and after-Treatment Systems with High-Dimensional Data: A Novel Fault-Relevant Feature Selection Method

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Qilan Ran ◽  
Yedong Song ◽  
Wenli Du ◽  
Wei Du ◽  
Xin Peng
Keyword(s):  
Genetic Algorithm ◽  
Diesel Engine ◽  
Fault Detection ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Diesel Engines ◽  
Canonical Correlation ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Relevant Variables

In order to reduce pollutants of the emission from diesel vehicles, complex after-treatment technologies have been proposed, which make the fault detection of diesel engines become increasingly difficult. Thus, this paper proposes a canonical correlation analysis detection method based on fault-relevant variables selected by an elitist genetic algorithm to realize high-dimensional data-driven faults detection of diesel engines. The method proposed establishes a fault detection model by the actual operation data to overcome the limitations of the traditional methods, merely based on benchmark. Moreover, the canonical correlation analysis is used to extract the strong correlation between variables, which constructs the residual vector to realize the fault detection of the diesel engine air and after-treatment system. In particular, the elitist genetic algorithm is used to optimize the fault-relevant variables to reduce detection redundancy, eliminate additional noise interference, and improve the detection rate of the specific fault. The experiments are carried out by implementing the practical state data of a diesel engine, which show the feasibility and efficiency of the proposed approach.

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 Compositional Canonical Correlation Analysis

2017 ◽  
Author(s):  
Jan Graffelman ◽  
Vera Pawlowsky-Glahn ◽  
Juan José Egozcue ◽  
Antonella Buccianti
Keyword(s):  
Trace Elements ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Covariance Matrices ◽  
Generalized Inverses ◽  
Geological Data ◽  
Data Set ◽  
X Ray ◽  
Log Ratio

AbstractThe study of the relationships between two compositions by means of canonical correlation analysis is addressed A coimnositional version of canonical correlation analysis is developed. and called CODA-CCO. We consider two approaches, using the centred log-ratio transformation and the calculation of all possible pairwise log-ratios within sets. The relationships between both approaches are pointed out, and their merits are discussed. The related covariance matrices are structurally singular, and this is efficiently dealt with by using generalized inverses. We develop compositional canonical biplots and detail their properties. The canonical biplots are shown to be powerful tools for discovering the most salient relationships between two compositions. Some guidelines for compositional canonical biplots construction are discussed. A geological data set with X-ray fluorescence spectrometry measurements on major oxides and trace elements is used to illustrate the proposed method. The relationships between an analysis based on centred log-ratios and on isometric log-ratios are also shown.

scholarly journals Canonical correlation analysis in high dimensions with structured regularization

2021 ◽  
pp. 1471082X2110410
Author(s):  
Elena Tuzhilina ◽  
Leonardo Tozzi ◽  
Trevor Hastie
Keyword(s):  
Data Structure ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Multivariate Data ◽  
High Dimensional Data ◽  
High Dimensional ◽  
High Dimensions ◽  
Structured Regularization ◽  
Data Matrices

Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an [Formula: see text] penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features equally, which can be ill-suited for some applications. In this article we introduce several approaches to regularizing CCA that take the underlying data structure into account. In particular, the proposed group regularized canonical correlation analysis (GRCCA) is useful when the variables are correlated in groups. We illustrate some computational strategies to avoid excessive computations with regularized CCA in high dimensions. We demonstrate the application of these methods in our motivating application from neuroscience, as well as in a small simulation example.

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