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Simultaneously Learning Adaptive Neighbors and Clustering Label via Semi-Supervised NMF

Published: 22 October 2019 Publication History

Abstract

Graph-based Nonnegative matrix factorization (NMF) methods usually construct an affinity graph in the original high-dimensional space, and then this graph is directly applied to the low-dimensional space, which has effectively improved the clustering performance. However, because of the curse of dimensionality, the graph may not precisely encode the geometric structure of samples in the low-dimensional space. In this paper, we propose a novel semi-supervised NMF method which can simultaneously learn adaptive neighbors and clustering label in the low-dimensional space. Specifically, the high-dimensional data are first projected onto the low-dimensional feature space, where the adaptive neighborhood weights of each feature are exploited to learn a flexible weight graph. Simultaneously, by incorporating the label information, the discriminative feature representations are learned, which assists the weight graph to better capture the potential semantic information. In addition, an efficient alternating minimization algorithm is introduced to solve the proposed model, and extensive experiments are constructed to validate the superiority of the proposed model compared with state-of-the-art methods on five real-word datasets.

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    CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering
    October 2019
    942 pages
    ISBN:9781450362948
    DOI:10.1145/3331453
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    Published: 22 October 2019

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    Author Tags

    1. Adaptive Neighbors
    2. Clustering
    3. NMF-based semi-supervised learning

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