Weighted Multi-View Data Clustering via Joint Non-Negative Matrix Factorization

In recent years, datasets which exist in present world are comprising of various representations of the data or in multiview environment, which frequently give the important data to each other. Multi-view clustering based on Non-negative matrix factorization (NMF) has turned to be a very hot directi...

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Vydáno v:2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) s. 1159 - 1165
Hlavní autoři: Khan, Ghufran Ahmad, Hu, Jie, Li, Tianrui, Diallo, Bassoma, Huang, Qianqian
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2019
Témata:
Convergence
Data mining
Design methodology
Dimensionality reduction
Intelligent systems
Knowledge engineering
multi-view clustering
non-negative matrix factorization
view weighting
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Shrnutí:In recent years, datasets which exist in present world are comprising of various representations of the data or in multiview environment, which frequently give the important data to each other. Multi-view clustering based on Non-negative matrix factorization (NMF) has turned to be a very hot direction of research in the field of Pattern Reognition, Machine Learning (ML), and data mining. and data mining due to unsupervised confuse information of Numerous Views. The main problem of employing NMF to multi-view clustering is how to define the factorizations to give significant and commensurate clustering solutions. Specially, multi-view clustering based NMF has achieved extensive attention due to its dimensionality reduction property. Existing methods based on NMF barely produced meaningful clustering solution from heterogeneous numerous views due to their complementary behaviors. To address this issue, we design a innovative NMF technique based Multiview clustering approach, which gives the more meaningful and compatible clustering solution over Numerous Views. The main outcome of the work, is to a design combined NMF method with view weight and constraint co-efficient which will bring the clustering solution to a common point for each view. The effectiveness of propose method is validated by conducting the experiments on real-world datasets.
DOI:10.1109/ISKE47853.2019.9170204