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SAC-NMF-Driven Graphical Feature Analysis and Applications

1
Department of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
2
Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, DUT-RU International School of Information and Software Engineering, Dalian University of Technology, Dalian 116026, China
3
School of Mathematical Science, Dalian University of Technology, Dalian 116026, China
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2020, 2(4), 630-646; https://doi.org/10.3390/make2040034
Received: 14 September 2020 / Revised: 13 November 2020 / Accepted: 24 November 2020 / Published: 8 December 2020
Feature analysis is a fundamental research area in computer graphics; meanwhile, meaningful and part-aware feature bases are always demanding. This paper proposes a framework for conducting feature analysis on a three-dimensional (3D) model by introducing modified Non-negative Matrix Factorization (NMF) model into the graphical feature space and push forward further applications. By analyzing and utilizing the intrinsic ideas behind NMF, we propose conducting the factorization on feature matrices constructed based on descriptors or graphs, which provides a simple but effective way to raise compressed and scale-aware descriptors. In order to enable part-aware model analysis, we modify the NMF model to be sparse and constrained regarding to both bases and encodings, which gives rise to Sparse and Constrained Non-negative Matrix Factorization (SAC-NMF). Subsequently, by adapting the analytical components (including hidden variables, bases, and encodings) to design descriptors, several applications have been easily but effectively realized. The extensive experimental results demonstrate that the proposed framework has many attractive advantages, such as being efficient, extendable, and so forth. View Full-Text
Keywords: non-negative matrix factorization; sparseness; bases; descriptors; graphical application non-negative matrix factorization; sparseness; bases; descriptors; graphical application
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MDPI and ACS Style

Li, N.; Wang, S.; Li, H.; Li, Z. SAC-NMF-Driven Graphical Feature Analysis and Applications. Mach. Learn. Knowl. Extr. 2020, 2, 630-646. https://doi.org/10.3390/make2040034

AMA Style

Li N, Wang S, Li H, Li Z. SAC-NMF-Driven Graphical Feature Analysis and Applications. Machine Learning and Knowledge Extraction. 2020; 2(4):630-646. https://doi.org/10.3390/make2040034

Chicago/Turabian Style

Li, Nannan, Shengfa Wang, Haohao Li, and Zhiyang Li. 2020. "SAC-NMF-Driven Graphical Feature Analysis and Applications" Machine Learning and Knowledge Extraction 2, no. 4: 630-646. https://doi.org/10.3390/make2040034

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