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  • 标题:Design of Multi-View Graph Embedding for Features Selection and Remotely Sensing Signal Classification
  • 其他标题:Design of Multi-View Graph Embedding for Features Selection and Remotely Sensing Signal Classification
  • 本地全文:下载
  • 作者:Abdullah Alhumaidi Alotaibi ; Sattam Alotaibi
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2020
  • 卷号:11
  • 期号:10
  • DOI:10.14569/IJACSA.2020.0111075
  • 出版社:Science and Information Society (SAI)
  • 摘要:Now-a-days, signal processing remains an intensive challenging area of research. In fact, various strategies have been suggested to address semi-supervised, feature selection and unlabeled samples challenges. The most frequent achievement was dedicated to exploit a single kind of feature/view from the original data. Recently, advanced techniques aimed to explore signals from different views and to, properly, integrate divergent kinds of interdependent features. In this paper, we propose a novel design of a multi-View Graph Embedding for features selection allowing a convenient integration of complementary weighted features. The proposed framework combines the singular properties of each feature space to accomplish a physically meaningful cooperative low-dimensional selection of input data. This allows us not only to perform a semi-supervised classification, but also to propagates narrow class information to unlabeled sample when only partial labeling knowledge is available. This paper makes the following contributions: (i) a feature selection schema for data refinement; and (ii) the adaptation of a multi-view graph-based approach by a better tackling of semi-supervised and dimensionality issues. Our experimental results, conducted by using a mixture of complementary features and aerial images datasets, demonstrate the effectiveness of the proposed framework without significantly increasing computational complexity.
  • 关键词:Signal processing; remote sensing images; features selection; graph embedding; unlabeled samples
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