Latent Feature Group Learning for High-Dimensional Data Clustering
AbstractIn this paper, we propose a latent feature group learning (LFGL) algorithm to discover the
feature grouping structures and subspace clusters for high-dimensional data. The feature grouping
structures, which are learned in an analytical way, can enhance the accuracy and efficiency of
high-dimensional data clustering. In LFGL algorithm, the Darwinian evolutionary process is used
to explore the optimal feature grouping structures, which are coded as chromosomes in the genetic
algorithm. The feature grouping weighting k-means algorithm is used as the fitness function to
evaluate the chromosomes or feature grouping structures in each generation of evolution. To better
handle the diverse densities of clusters in high-dimensional data, the original feature grouping
weighting k-means is revised with the mass-based dissimilarity measure rather than the Euclidean
distance measure and the feature weights are optimized as a nonnegative matrix factorization
problem under the orthogonal constraint of feature weight matrix. The genetic operations of mutation
and crossover are used to generate the new chromosomes for next generation. In comparison
with the well-known clustering algorithms, LFGL algorithm produced encouraging experimental
results on real world datasets, which demonstrated the better performance of LFGL when clustering
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Wang, W.; He, Y.; Ma, L.; Huang, J.Z.Z. Latent Feature Group Learning for High-Dimensional Data Clustering. Information 2019, 10, 208.
Wang W, He Y, Ma L, Huang JZZ. Latent Feature Group Learning for High-Dimensional Data Clustering. Information. 2019; 10(6):208.Chicago/Turabian Style
Wang, Wenting; He, Yulin; Ma, Liheng; Huang, Joshua Zhexue Z. 2019. "Latent Feature Group Learning for High-Dimensional Data Clustering." Information 10, no. 6: 208.
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