Abstract
Dimension reduction techniques are very important, as high-dimensional data are ubiquitous in many real-world applications, especially in this era of big data. In this paper, we propose a novel supervised dimensionality reduction method, called appropriate points choosing based DAG-DNE (Apps-DAG-DNE). In Apps-DAG-DNE, we choose appropriate points to construct adjacency graphs, for example, it chooses nearest neighbors to construct inter-class graph, which can build a margin between samples if they belong to the different classes, and chooses farthest points to construct intra-class graph, which can establish relationships between remote samples if and only if they belong to the same class. Thus, Apps-DAG-DNE could find a good representation for original data. To investigate the performance of Apps-DAG-DNE, we compare it with the state-of-the-art dimensionality reduction methods on Caltech-Leaves and Yale datasets. Extensive experimental demonstrates that the proposed Apps-DAG-DNE outperforms other dimensionality reduction methods and achieves state-of-the-art performance for image classification.
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This work was supported by the National Science Foundation of China (61571066 and 61472047).
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Ding, C., Wang, S. Appropriate points choosing for subspace learning over image classification. J Supercomput 75, 688–703 (2019). https://doi.org/10.1007/s11227-018-2687-9
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DOI: https://doi.org/10.1007/s11227-018-2687-9