基于原始应力准则的JOFC快速嵌入
Fast Embedding for JOFC Using the Raw Stress Criterion
摘要：
保真度和可公度联合优化（JOFC）流形匹配方法嵌入了一个由同一组对象上的多个相异性组成的综合相异性矩阵。这种嵌入的一种方法通过连续的Guttman变换对原始应力误差标准进行迭代优化，优化了对每个个体相异性矩阵的保真度的保持以及跨模态的每个给定观察的可验证性。在本文中，我们利用JOFC固有的特殊结构，准确，有效地计算连续的Guttman变换，因此，我们能够大大加快JOFC过程中的样本内和样本外嵌入。我们证明了我们的实现在真实和模拟数据的例子的可扩展性。
英文摘要：
The Joint Optimization of Fidelity and Commensurability (JOFC) manifold matching methodology embeds an omnibus dissimilarity matrix consisting of multiple dissimilarities on the same set of objects. One approach to this embedding optimizes the preservation of fidelity to each individual dissimilarity matrix together with commensurability of each given observation across modalities via iterative majorization of a raw stress error criterion by successive Guttman transforms. In this paper, we exploit the special structure inherent to JOFC to exactly and efficiently compute the successive Guttman transforms, and as a result we are able to greatly speed up the JOFC procedure for both in-sample and out-of-sample embedding. We demonstrate the scalability of our implementation on both real and simulated data examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
