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含离群点的子空间聚类的几何分析AI 解读

This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower-dimensional planes. As is common in computer vision or unsupervised learning applications, we do not know in advance how many subspaces there are nor do we have any information about their dimensions. We develop a novel geometric analysis of an algorithm named sparse subspace clustering (SSC) [In IEEE Conference on Computer Vision and Pattern Recognition,…

讨论：通过凸优化选择潜变量图模型AI 解读

We wish to congratulate the authors for their innovative contribution, which is bound to inspire much further research.We find latent variable model selection to be a fantastic application of matrix decomposition methods, namely, the superposition of low-rank and sparse elements.Clearly, the methodology introduced in this paper is of potential interest across many disciplines.In the following, we will first discuss this paper in more detail and then reflect on the…