Abstract
Nonnegative matrix factorization, as a classical part-based representation method, has been widely used in pattern recognition, data mining and other fields. However, the traditional nonnegative matrix factorization directly factoring decomposes the original data, and the original data often contains a lot of redundancy and noise, which seriously affect the subsequent processing of the data. In this work, we propose an adaptive graph regularization discriminant nonnegative matrix factorization (AGDNMF) for image clustering. The AGDNMF algorithm makes full use of local structure information and a small amount of label information. In AGDNMF, the local structure information can be more accurate and the label information can prevent the points with the same label from being merged into one point. These two items are combined into the objective function of NMF. In addition, we provide the update rules for the corresponding optimization functions and prove its convergence. A large number of experiments on different data sets show that the proposed algorithm has good clustering performance.
Highlights
In today’s information age, we can get the information we want regardless of time or place
We propose a semisupervised adaptive graph regularization discriminant nonnegative matrix factorization (AGDNMF)
In this paper, we propose a novel semi-supervised adaptive graph regularization discriminant nonnegative matrix factorization (AGDNMF)
Summary
In today’s information age, we can get the information we want regardless of time or place. By using the label information and the non-negative coefficient matrix to construct the regularization constraints, Babaee and Tsoukalas [26] propose discriminative NMF (DNMF). Combined with local geometry structure, Cai et al [34] proposed graph regularized nonnegative matrix factorization (GNMF). In the real world, the original data usually contains a small amount of label information Inspired by these conditions, this paper proposes a new algorithm called adaptive graph regularized discriminate nonnegative matrix factorization (AGDNMF). Adaptive to obtain the constructed neighbor graph This can better capture local structural information and get better recognition performance.
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