Orthogonal Nonnegative Matrix Factorization using a novel deep Autoencoder Network

作     者：Yang, Mingming Xu, Songhua 

作者机构：Xi An Jiao Tong Univ Sch Math & Stat Xian Peoples R China 

出 版 物：《KNOWLEDGE-BASED SYSTEMS》 (Knowl Based Syst)

年 卷 期：2021年第227卷

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Science and Technology Pro-gram of the Chinese Ministry of Science and Technology [2020AAA0106302] National Natural Science Foundation of China [61876150, 12026609] Key Re-search and Development Program of Shaanxi Province [2021SF188] Science and Technology Program of the City of Xi'an [20YXYJ0009-12, XA2020RKXYJ0105] 

主　　题：Orthogonal Nonnegative Matrix Factorization Deep autoencoder network Auxiliary function Multiplication update rule 

摘      要：Orthogonal Nonnegative Matrix Factorization (ONMF) offers an important analytical vehicle for addressing many problems. Encouraged by record-breaking successes attained by neural computing models in solving an assortment of data analytics tasks, a rich collection of neural computing models has been proposed to perform ONMF with compelling performance. Such existing models can be broadly classified into the shallow-layered structure (SLS) based and deep-layered structure (DLS) based models. However, SLS models cannot capture complex relationships and hierarchical information latent in a matrix due to their simple network structures and DLS models rely on an iterative procedure to derive weights, leading to a less efficient solution process and cannot be reused to factorize new matrices. To overcome these shortcomings, this paper proposes a novel deep autoencoder network for ONMF, which is abbreviated as DAutoED-ONMF. Compared with SLS models, the newly proposed model is capable of generating solutions with good interpretability and solution uniqueness like original SLS models, yet the new model attains a superior learning capability thanks to its deep structure employed. In comparison with DLS models, the new model trains a reusable encoder network to directly factorize any given matrix with no need to repeatedly retrain the model for factorizing multiple matrices using a tailor-designed network training procedure. Proof of the procedure s convergence is presented with an analysis of its computational complexity. The numerical experiments conducted on several publicly data sets convincingly demonstrate that the proposed DAutoED-ONMF model gains promising performance in terms of multiple metrics. (C) 2021 Elsevier B.V. All rights reserved.