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L1-Subspace Tracking for Streaming Data
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DOI：10.1016/j.patcog.2019.106992.png)
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摘要
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High-dimensional data usually exhibit intrinsic low-rank structures. With tremendous amount of streaming data generated by ubiquitous sensors in the world of Internet-of-Things, fast detection of such low-rank pattern is of utmost importance to a wide range of applications. In this work, we present an L-1-subspace tracking method to capture the low-rank structure of streaming data. The method is based on the L-1-norm principal-component analysis (L-1-PCA) theory that offers outlier resistance in subspace calculation. The proposed method updates the L-1-subspace as new data are acquired by sensors. In each time slot, the conformity of each datum is measured by the L-1-subspace calculated in the previous time slot and used to weigh the datum. Iterative weighted L-1-PCA is then executed through a refining function. The superiority of the proposed L-1-subspace tracking method compared to existing approaches is demonstrated through experimental studies in various application fields. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Dimensionality reduction
Eigenvector decomposition
Internet-of-Things
L-1-norm
Outliers
Principal-component analysis
Subspace learning
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7.6
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被引数:
4.5W
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