<italic>l</italic>_{2,<italic>p</italic>}-Norm Based Discriminant Subspace Clustering Algorithm

Discriminative subspace clustering (DSC) combines Linear Discriminant Analysis (LDA) with clustering algorithm, such as K-means (KM), to form a single framework to perform dimension reduction and clustering simultaneously. It has been verified to be effective for high-dimensional data. However, most...

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書誌詳細

主要な著者:	Xiaobin Zhi, Longtao Bi, Jiulun Fan
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2020-01-01
シリーズ:	IEEE Access
主題:	Subspace clustering linear discriminant analysis <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l</italic>₂,<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₚ</italic>-norm iterative reweighted least squares robustness
オンライン･アクセス:	https://ieeexplore.ieee.org/document/9072148/
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