CCA: AnRPackage to Extend Canonical Correlation Analysis
Journal of Statistical Software, 2008 Canonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the ...
González, Ignacio +3 more
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A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials. [PDF]
PLoS ONE, 2015 Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs).
Masaki Nakanishi +3 more
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Scalable multi-label canonical correlation analysis for cross-modal retrieval [PDF]
, 2023 Multi-label canonical correlation analysis (ml-CCA) has been developed for cross-modal retrieval. However, the computation of ml-CCA involves dense matrices eigendecomposition, which can be computationally expensive.
Zhao, Guoying, Shu, Xin
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D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets [PDF]
Journal of the American Statistical Association, 2019 A typical approach to the joint analysis of two high-dimensional datasets is to decompose each data matrix into three parts: a low-rank common matrix that captures the shared information across datasets, a low-rank distinctive matrix that characterizes the individual information within a single dataset, and an additive noise matrix.
Hai Shu, Xiao Wang, Hongtu Zhu
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Frontiers in Neuroscience, 2023 ObjectiveCompared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications.
Ruiquan Chen +6 more
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Canonical correlation analysis as a statistical method to relate underwater acoustic propagation and ocean fluctuations [PDF]
JASA Express Letters, 2022 Numerical models are currently used to understand how environmental fluctuations impact acoustic propagation. Such a process can be tedious in complex fluctuating environments.
Alexandre L'Her +5 more
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Research on Unsupervised Classification Algorithm Based on SSVEP
Applied Sciences, 2022 Filter Bank Canonical Correlation Analysis (FBCCA) is used to classify electroencephalography (EEG) signals to overcome insufficient training data for EEG signal classification.
Yingnian Wu +4 more
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The Great Bustard (<i>Otis tarda</i>) and Common Crane (<i>Grus grus</i>) Utilize Food Resources via Gut Microbiota Remodeling During Wintering in the Yellow River Wetlands in Ordos City, Inner Mongolia, China. [PDF]
Ecol EvolThis study detected the diet and gut microbiota of great bustards and common cranes in the wintering duration in the Yellow River Wetlands of Inner Mongolia using high‐throughput sequencing technology. This study indirectly indicated that great bustards and common cranes are well‐adapted to the environment of the Yellow River Wetlands during the ...
Gao L, Fang H, Pei H, Li J, Liu L.
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Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview
CoRR, 2019 Multi-view learning (MVL) is a strategy for fusing data from different sources or subsets. Canonical correlation analysis (CCA) is very important in MVL, whose main idea is to map data from different views onto a common space with maximum correlation. Traditional CCA can only be used to calculate the linear correlation of two views.
Chenfeng Guo, Dongrui Wu
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Image Retrieval via Canonical Correlation Analysis and Binary Hypothesis Testing
Information, 2022 Canonical Correlation Analysis (CCA) is a classic multivariate statistical technique, which can be used to find a projection pair that maximally captures the correlation between two sets of random variables.
Kangdi Shi +6 more
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