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Human collective intelligence inspired multi-view representation learning—Enabling view communication by simulating human communication mechanism
In real-world applications, we often encounter multi-view learning tasks where we need to
learn from multiple sources of data or use multiple sources of data to make decisions. Multi …
learn from multiple sources of data or use multiple sources of data to make decisions. Multi …
SDGCCA: supervised deep generalized canonical correlation analysis for multi-omics integration
Integration of multi-omics data provides opportunities for revealing biological mechanisms
related to certain phenotypes. We propose a novel method of multi-omics integration called …
related to certain phenotypes. We propose a novel method of multi-omics integration called …
Co-embedding: a semi-supervised multi-view representation learning approach
Learning an expressive representation from multi-view data is a crucial step in various real-
world applications. In this paper, we propose a semi-supervised multi-view representation …
world applications. In this paper, we propose a semi-supervised multi-view representation …
A complete canonical correlation analysis for multiview learning
Canonical correlation analysis (CCA) is an effective feature learning method, which has
wide applications in pattern recognition and computer vision. However, CCA considers the …
wide applications in pattern recognition and computer vision. However, CCA considers the …
A new robust deep canonical correlation analysis algorithm for small sample problems
As a nonlinear feature learning method, deep canonical correlation analysis (DCCA) has got
a great success in computer vision. Compared with kernel methods, deep neural networks …
a great success in computer vision. Compared with kernel methods, deep neural networks …
An Improved Canonical Correlation Analysis Method with Adaptive Graph Learning
C Yuan, S Hou - Advances in Natural Computation, Fuzzy Systems and …, 2022 - Springer
Graph learning describes the local structure information hidden in samples by adjacent
matrices and the key step is determining the nearest samples where the local and sparse …
matrices and the key step is determining the nearest samples where the local and sparse …
Time efficient and novel ways of analyzing high-dimensional multi-omics datasets: parallel computing and multi-view learning
R Shikder - 2019 - mspace.lib.umanitoba.ca
Due to the advancements in high-throughput sequencing technologies high-dimensional
omics data are rapidly increasing in number and require enhanced computational power to …
omics data are rapidly increasing in number and require enhanced computational power to …