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Linear discriminant analysis guided by unsupervised ensemble learning. (English) Zbl 1443.68145
Summary: The high dimensionality and sparsity of data often increase the complexity of clustering; these factors occur simultaneously in unsupervised learning. Clustering and linear discriminant analysis (LDA) are methods to reduce the dimensionality and sparsity of data. In this study, the similarity of clustering and LDA are investigated based on their objective functions. Subsequently, their objective functions are integrated, and an LDA guided by an unsupervised ensemble learning (LDA-UEL) model is proposed. To create the proposed model, fuzziness \(F\) is designed to measure the confidence of unsupervised learning and the inference of the proposed model is illustrated. Furthermore, a corresponding algorithm for the inference is designed. Finally, extensive experiments are designed, and the results thus obtained demonstrate the effectiveness and high performance of the LDA-UEL model.
68T05 Learning and adaptive systems in artificial intelligence
62H30 Classification and discrimination; cluster analysis (statistical aspects)
apcluster; APCluster
Full Text: DOI
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