A Semi-NMF-PCA Unified Framework for Data Clustering

In this work, we propose a novel way to consider the clustering and the reduction of the dimension simultaneously. Indeed, our approach takes advantage of the mutual reinforcement between data reduction and clustering tasks. The use of a low-dimensional representation can be of help in providing sim...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2017-01, Vol.29 (1), p.2-16
Main Authors: Allab, Kais, Labiod, Lazhar, Nadif, Mohamed
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Clustering methods
Computer Science
Data reduction
dimension reduction
Linear programming
locality preserving
Machine Learning
Matrix decomposition
Optimization
Partitioning algorithms
Principal component analysis
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Summary:In this work, we propose a novel way to consider the clustering and the reduction of the dimension simultaneously. Indeed, our approach takes advantage of the mutual reinforcement between data reduction and clustering tasks. The use of a low-dimensional representation can be of help in providing simpler and more interpretable solutions. We show that by doing so, our model is able to better approximate the relaxed continuous dimension reduction solution by the true discrete clustering solution. Experiment results show that our method gives better results in terms of clustering than the state-of-the-art algorithms devoted to similar tasks for data sets with different proprieties.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2016.2606098