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An Effective Clustering Optimization Method for Unsupervised Linear Discriminant Analysis
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DOI：10.1109/TKDE.2021.3124023.png)
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The recent work Unsupervised Linear Discriminant Analysis (Un-LDA) completes its clustering process during the alternating optimization by converting equivalently the objective and finally using the K-means algorithm. However, the K-means algorithm has its inherent drawbacks. It is hard for the K-means algorithm to deal well with some complex clustering cases where there are too many real clusters or non-convex clusters. In this paper, a novel clustering optimization method is presented to accomplish the clustering process in Un-LDA and the resulting method can be named Un-LDA(CD). Specifically, instead of the K-means algorithm, an elaborately designed coordinate descent algorithm is adopted to obtain the clusters after the objective function goes through a series of simple but deft equivalent conversions. Extensive experiments have demonstrated that the coordinate descent clustering solution for Un-LDA can outperform the original K-means based solution on the tested data sets especially those complex data sets with a pretty large number of real clusters.
Keyword:
Dimensionality reduction
Clustering methods
Clustering algorithms
Linear programming
Task analysis
Optimization methods
Linear discriminant analysis
Clustering optimization method
unsupervised learning
coordinate descent
dimensionality reduction
linear discriminant analysis
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
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