论文标题
通过深度集体矩阵三因素化的增强多视图数据的多路光谱聚类
Multi-way Spectral Clustering of Augmented Multi-view Data through Deep Collective Matrix Tri-factorization
论文作者
论文摘要
我们介绍了第一个基于深度学习的体系结构，用于矩阵的任意集合（也称为增强的多视图数据）的集体矩阵三个基质化（DCMTF）。 DCMTF可用于关系数据矩阵的异质集合的多路光谱聚类，以发现每个尺寸的每个输入矩阵中的潜在簇，以及跨群集的关联的优势。 DCMTF的源代码可在我们的公共存储库中找到：https：//bitbucket.org/cdal/dcmtf_generic
We present the first deep learning based architecture for collective matrix tri-factorization (DCMTF) of arbitrary collections of matrices, also known as augmented multi-view data. DCMTF can be used for multi-way spectral clustering of heterogeneous collections of relational data matrices to discover latent clusters in each input matrix, across both dimensions, as well as the strengths of association across clusters. The source code for DCMTF is available on our public repository: https://bitbucket.org/cdal/dcmtf_generic
