General Tensor Spectral Co-clustering for Higher-Order Data
نویسندگان
چکیده
Spectral clustering and co-clustering are well-known techniques in data analysis, and recent work has extended spectral clustering to square, symmetric tensors and hypermatrices derived from a network. We develop a new tensor spectral co-clustering method that simultaneously clusters the rows, columns, and slices of a nonnegative three-mode tensor and generalizes to tensors with any number of modes. The algorithm is based on a new random walk model which we call the super-spacey random surfer. We show that our method out-performs state-of-the-art co-clustering methods on several synthetic datasets with ground truth clusters and then use the algorithm to analyze several real-world datasets.
منابع مشابه
The Sign-Real Spectral Radius for Real Tensors
In this paper a new quantity for real tensors, the sign-real spectral radius, is defined and investigated. Various characterizations, bounds and some properties are derived. In certain aspects our quantity shows similar behavior to the spectral radius of a nonnegative tensor. In fact, we generalize the Perron Frobenius theorem for nonnegative tensors to the class of real tensors.
متن کاملTensor Spectral Clustering for Partitioning Higher-order Network Structures
Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) ...
متن کاملHybrid Clustering of Multiple Information Sources via HOSVD
We present a hybrid clustering algorithm of multiple information sources via tensor decomposition, which can be regarded an extension of the spectral clustering based on modularity maximization. This hybrid clustering can be solved by the truncated higher-order singular value decomposition (HOSVD). Experimental results conducted on the synthetic data have demonstrated the effectiveness. keyword...
متن کاملIntegration of Single-view Graphs with Diffusion of Tensor Product Graphs for Multi-view Spectral Clustering
Multi-view clustering takes diversity of multiple views (representations) into consideration. Multiple views may be obtained from various sources or different feature subsets and often provide complementary information to each other. In this paper, we propose a novel graph-based approach to integrate multiple representations to improve clustering performance. While original graphs have been wid...
متن کاملDynamic Tensor Clustering
Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. Also there is often a gap between statistical guarantee and computational efficiency for existing tensor clustering solutions. In this article, we aim to bridge this gap by proposing a ne...
متن کامل