论文标题
星形绘图多模式匹配组件分析用于数据融合和传输学习
Star-Graph Multimodal Matching Component Analysis for Data Fusion and Transfer Learning
论文作者
论文摘要
以前的匹配组件分析（MCA）技术将两个数据域映射到一个公共域，以在数据融合和传输学习环境中进行进一步处理。在本文中，我们将这些技术扩展到星形绘图多模式（SGM）情况，其中一个特定的数据域通过目标函数连接到$ m $。我们为封闭形式的最大化问题和算法提供了一个特殊的可行点，以进行计算和迭代改进，从而导致我们的主要结果SGM MAPS。我们还提供了数值示例，证明SGM能够在很少的培训点可用时能够比MCA编码更多的信息。此外，我们对MCA协方差约束进行进一步的概括，消除了以前的可行性条件，并允许规定的协方差矩阵等级的较大值。
Previous matching component analysis (MCA) techniques map two data domains to a common domain for further processing in data fusion and transfer learning contexts. In this paper, we extend these techniques to the star-graph multimodal (SGM) case in which one particular data domain is connected to $m$ others via an objective function. We provide a particular feasible point for the resulting trace maximization problem in closed form and algorithms for its computation and iterative improvement, leading to our main result, the SGM maps. We also provide numerical examples demonstrating that SGM is capable of encoding into its maps more information than MCA when few training points are available. In addition, we develop a further generalization of the MCA covariance constraint, eliminating a previous feasibility condition and allowing larger values of the rank of the prescribed covariance matrix.
