777 research outputs found
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction
Most methods for dimensionality reduction are based on either tensor
representation or local geometry learning. However, the tensor-based methods
severely rely on the assumption of global and multilinear structures in
high-dimensional data; and the manifold learning methods suffer from the
out-of-sample problem. In this paper, bridging the tensor decomposition and
manifold learning, we propose a novel method, called Hypergraph Regularized
Nonnegative Tensor Factorization (HyperNTF). HyperNTF can preserve
nonnegativity in tensor factorization, and uncover the higher-order
relationship among the nearest neighborhoods. Clustering analysis with HyperNTF
has low computation and storage costs. The experiments on four synthetic data
show a desirable property of hypergraph in uncovering the high-order
correlation to unfold the curved manifolds. Moreover, the numerical experiments
on six real datasets suggest that HyperNTF robustly outperforms
state-of-the-art algorithms in clustering analysis.Comment: 12 pages, 6 figures, 9 table
- …