摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-计算的新研究-神经计算是一篇报道的主题。根据NewsRx编辑在《中华人民共和国上海日报》上的报道，研究表明：“稀疏相关分析(CCA)是检测稀疏结构下潜在信息的一种有用的统计工具，但是稀疏CA只适用于两个数据集，即，稀疏CA可以将稀疏结构视为典型变量的拉普拉斯先验。”只有两个视图或两个不同的对象。我们的新闻记者引用了上海交通大学的一篇研究，“为了克服这一局限性，我们提出了一种稀疏广义Canoni Cal相关分析(GCCA)，它可以检测具有稀疏结构的多视图数据之间的潜在关系。”本文将GCCA转化为线性AR方程组,并施加$ELL_1极小惩罚以追求稀疏性,从而导致Stiefel流形上的一个非凸问题.基于一致性优化,发展了一种有分布的交替迭代方法,并在温和条件下详细研究了一致性问题.
Abstract
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.”
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