Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2019

Abstract

Sparse data is known to pose challenges to cluster analysis, as the similarity between data tends to be ill-posed in the high-dimensional Hilbert space. Solutions in the literature typically extend either k-means or spectral clustering with additional steps on representation learning and/or feature weighting. However, adding these usually introduces new parameters and increases computational cost, thus inevitably lowering the robustness of these algorithms when handling massive ill-represented data. To alleviate these issues, this paper presents a class of self-organizing neural networks, called the salience-aware adaptive resonance theory (SA-ART) model. SA-ART extends Fuzzy ART with measures for cluster-wise salient feature modeling. Specifically, two strategies, i.e. cluster space matching and salience feature weighting, are incorporated to alleviate the side-effect of noisy features incurred by high dimensionality. Additionally, cluster weights are bounded by the statistical means and minimums of the samples therein, making the learning rate also self-adaptable. Notably, SA-ART allows clusters to have their own sets of self-adaptable parameters. It has the same time complexity of Fuzzy ART and does not introduce additional hyperparameters that profile cluster properties. Comparative experiments have been conducted on the ImageNet and BlogCatalog datasets, which are large-scale and include sparsely-represented data. The results show that, SA-ART achieves 51.8% and 18.2% improvement over Fuzzy ART, respectively. While both have a similar time cost, SA-ART converges faster and can reach a better local minimum. In addition, SA-ART consistently outperforms six other state-of-the-art algorithms in terms of precision and F1 score. More importantly, it is much faster and exhibits stronger robustness to large and complex data.

Keywords

Adaptive resonance theory, Clustering, Sparse data, Subspace learning, Feature weighting, Parameter adaptation

Discipline

Databases and Information Systems | OS and Networks | Software Engineering

Research Areas

Data Science and Engineering

Publication

Neural Networks

Volume

120

First Page

143

Last Page

157

ISSN

0893-6080

Identifier

10.1016/j.neunet.2019.09.014

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.neunet.2019.09.014

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