A Generalized Deep Learning Clustering Algorithm Based on Non-Negative Matrix Factorization

Clustering is a popular research topic in the field of data mining, in which the clustering method based on non-negative matrix factorization (NMF) has been widely employed. However, in the update process of NMF, there is no learning rate to guide the update as well as the update depends on the data itself, which leads to slow convergence and low clustering accuracy. To solve these problems, a generalized deep learning clustering (GDLC) algorithm based on NMF is proposed in this article. Firstly, a nonlinear constrained NMF (NNMF) algorithm is constructed to achieve sequential updates of the elements in the matrix guided by the learning rate. Then, the gradient values corresponding to the element update are transformed into generalized weights and generalized biases, by inputting the elements as well as their corresponding generalized weights and generalized biases into the nonlinear activation function to construct the GDLC algorithm. In addition, for improving the understanding of the GDLC algorithm, its detailed inference procedure and algorithm design are provided. Finally, the experimental results on eight datasets show that the GDLC algorithm has efficient performance.

Automated summary

In this article, a Generalized Deep Learning Clustering (GDLC) algorithm based on Non-Negative Matrix Factorization (NMF) is proposed to address the slow convergence and low clustering accuracy in the update process of NMF. A nonlinear constrained NMF (NNMF) algorithm is constructed to achieve sequential updates of the matrix elements guided by the learning rate, and the GDLC algorithm is constructed by transforming gradient values into generalized weights and biases through a nonlinear activation function. The experimental results on eight datasets demonstrate the efficient performance of the GDLC algorithm.