Nonnegative Low Rank Tensor Approximation and its Application to Multi-dimensional Images

• URL: http://arxiv.org/abs/2007.14137v2
• Date: Sun, 26 Sep 2021 13:46:21 GMT
• Title: Nonnegative Low Rank Tensor Approximation and its Application to Multi-dimensional Images
• Authors: Tai-Xiang Jiang, Michael K. Ng, Junjun Pan, Guangjing Song
• Abstract summary: Nonnegativity is one of the important property as each pixel value refers to nonzero light intensity in image data acquisition. We propose an alternating projections algorithm for computing such nonnegative low rank tensor approximation. Experimental results for synthetic data and multi-dimensional images are presented to demonstrate the performance of NLRT is better than state-of-the-art NTF methods.
• Score: 24.140129751739007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The main aim of this paper is to develop a new algorithm for computing nonnegative low rank tensor approximation for nonnegative tensors that arise in many multi-dimensional imaging applications. Nonnegativity is one of the important property as each pixel value refers to nonzero light intensity in image data acquisition. Our approach is different from classical nonnegative tensor factorization (NTF) which requires each factorized matrix and/or tensor to be nonnegative. In this paper, we determine a nonnegative low Tucker rank tensor to approximate a given nonnegative tensor. We propose an alternating projections algorithm for computing such nonnegative low rank tensor approximation, which is referred to as NLRT. The convergence of the proposed manifold projection method is established. Experimental results for synthetic data and multi-dimensional images are presented to demonstrate the performance of NLRT is better than state-of-the-art NTF methods.

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