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Sparse Weighted Canonical Correlation Analysis [PDF]
Chinese Journal of Electronics, 2018 8 pages, 5 ...
Wenwen Min, Juan Liu, Shihua Zhang
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Canonical sparse cross-view correlation analysis
Neurocomputing, 2016Abstract Recently, multi-view feature extraction has attracted great interest and Canonical Correlation Analysis (CCA) is a powerful technique for finding the linear correlation between two view variable sets. However, CCA does not consider the structure and cross view information in feature extraction, which is very important for subsequence tasks ...
Chen Zu, Daoqiang Zhang
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Sparse Canonical Correlation Analysis: New Formulation and Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2)
Delin Chu, Li-Zhi Liao, Michael K Ng
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Branch-and-bound algorithm for optimal sparse canonical correlation analysis
Expert Systems With Applications, 2023Akihisa Watanabe +2 more
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Sparse canonical correlation analysis for recognition
Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, 2015Canonical correlation analysis (CCA) is one promising feature extraction and subspace learning method for multivariate vectors by exploiting the correlation between two multidimensional varia-bles in a linear way. Hence CCA has been widely employed in many applications such as statistics, economics and signal pro-cessing.
Huimin Zhang +2 more
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Sparse Canonical Correlation Analysis
2020In clinical research big data are particularly observed with genetic studies of the relationships between the genome and clinical outcomes like metabolics and more. With genome wide research we have to take into account the presence of 250.0000 genes in every human cell, and 2000 nucleic acids in every gene.
Ton J. Cleophas, Aeilko H. Zwinderman
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Music Emotion Recognition through Sparse Canonical Correlation Analysis
2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR), 2021For centuries, music has been an important part of various cultures and a special language for humans to express their thoughts and emotions. Music emotion plays an important role in music retrieval, mood detection and other music-related applications. Music emotion recognition (MER) has become a research hotspot in the world.
Hongwei Li 0024 +4 more
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Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
Statistical Applications in Genetics and Molecular Biology, 2009Large scale genomic studies with multiple phenotypic or genotypic measures may require the identification of complex multivariate relationships. In multivariate analysis a common way to inspect the relationship between two sets of variables based on their correlation is canonical correlation analysis, which determines linear combinations of all ...
Joseph Beyene
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FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics
IEEE Transactions on Medical Imaging, 2018 Reducing the number of false discoveries is presently one of the most pressing issues in the life sciences. It is of especially great importance for many applications in neuroimaging and genomics, where datasets are typically high-dimensional, which means that the number of explanatory variables exceeds the sample size.
Alexej Gossmann +2 more
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Weight-based canonical sparse cross-view correlation analysis
Pattern Analysis and Applications, 2017As a powerful method for multi-view feature extraction, canonical correlation analysis (CCA) can find linear correlation relationship between feature sets from two views. However, CCA has two disadvantages. One is CCA cannot find nonlinear correlation relationship and the local structure of features; the other is CCA does not consider the structure and
Changming Zhu, Rigui Zhou, Chen Zu
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