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Canonical Correlation Analysis

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A technical review of canonical correlation analysis for neuroscience applications.

Human brain mapping
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analys...

Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data.

Computational and mathematical methods in medicine
The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity increases year by year. The lipids which influence acne to a large extent are studied by metagenomic ...

A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis.

Neural computation
Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), ...

A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19.

Computers in biology and medicine
The world has experienced epidemics of coronavirus infections several times over the last two decades. Recent studies have shown that using medical imaging techniques can be useful in developing an automatic computer-aided diagnosis system to detect ...

A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals. Despite improving the speech quality, su...

A Single-Side Neural Network-Aided Canonical Correlation Analysis With Applications to Fault Diagnosis.

IEEE transactions on cybernetics
Recently, canonical correlation analysis (CCA) has been explored to address the fault detection (FD) problem for industrial systems. However, most of the CCA-based FD methods assume both Gaussianity of measurement signals and linear relationships amo...

A Comparative Study of Deep Neural Network-Aided Canonical Correlation Analysis-Based Process Monitoring and Fault Detection Methods.

IEEE transactions on neural networks and learning systems
Multivariate analysis is an important kind of method in process monitoring and fault detection, in which the canonical correlation analysis (CCA) makes use of the correlation change between two groups of variables to distinguish the system status and...

ReGeNNe: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Common human diseases result from the interplay of genes and their biologically associated pathways. Genetic pathway analyses provide more biological insight as compared to conventional gene-based analysis. In this article, we propose a f...

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.

BMC bioinformatics
BACKGROUND: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large num...