Semi-supervised bi-orthogonal constraints dual-graph regularized NMF for subspace clustering

Non-negative matrix factorization (NMF), as an explanatory feature extraction technology, has powerful dimensionality reduction and semantic representation capabilities. In recent years, it has attracted great attention in the process of dimensionality reduction analysis of real high-dimensional data. However, the classic NMF algorithm is an unsupervised method in terms of learning methods. In the calculation process, the spatial structure information in the original data is often ignored, resulting in poor clustering performance of the algorithm in the subspace. In order to overcome the above problems, this paper proposes a semi-supervised NMF algorithm called semi-supervised dual graph regularized NMF with biorthogonal constraints (SDGNMF-BO). In this algorithm, the semi-supervised NMF three-factor decomposition is based on the dual graph model of the data space and feature space of the original data, which can effectively improve the learning ability of the algorithm in the subspace, and the biorthogonal constraint conditions are added in the decomposition process and achieve better local representation, significantly reduce the inconsistency between the original matrix and the basic vector. In order to prove the superiority of the algorithm under fair conditions, compares the multi-directional clustering experiments of 4 real image data sets and 1 text data set, and uses 2 clustering evaluation indexes to prove that the algorithm is better than other comparison algorithms.