首页|Shanghai Jiao Tong University Reports Findings in Neural Computation (Sparse Gen eralized Canonical Correlation Analysis: Distributed Alternating Iteration-Based Approach)
Shanghai Jiao Tong University Reports Findings in Neural Computation (Sparse Gen eralized Canonical Correlation Analysis: Distributed Alternating Iteration-Based Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Computation - Neural C omputation is the subject of a report. According to news reporting out of Shangh ai, People’s Republic of China, by NewsRx editors, research stated, “Sparse cano nical correlation analysis (CCA) is a useful statistical tool to detect latent i nformation with sparse structures. However, sparse CCA, where the sparsity could be considered as a Laplace prior on the canonical variates, works only for two data sets, that is, there are only two views or two distinct objects.” Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “To overcome this limitation, we propose a sparse generalized canoni cal correlation analysis (GCCA), which could detect the latent relations of mult iview data with sparse structures. Specifically, we convert the GCCA into a line ar system of equations and impose $\ell _ 1$ minimization penalty to pursue sparsity. This results in a nonc onvex problem on the Stiefel manifold. Based on consensus optimization, a distri buted alternating iteration approach is developed, and consistency is investigat ed elaborately under mild conditions.”
ShanghaiPeople’s Republic of ChinaAs iaComputationCorrelation AnalysisEmerging TechnologiesMachine LearningNeural Computation
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