Absent Multiple Kernel Learning Algorithms

Multiple kernel learning (MKL) has been intensively studied during the past decade. It optimally combines the multiple channels of each sample to improve classification performance. However, existing MKL algorithms cannot effectively handle the situation where some channels of the samples are missin...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 42; H. 6; S. 1303 - 1316
Hauptverfasser:	Liu, Xinwang, Wang, Lei, Zhu, Xinzhong, Li, Miaomiao, Zhu, En, Liu, Tongliang, Liu, Li, Dou, Yong, Yin, Jianping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Absent data learning Algorithms Channels Classification algorithms Clustering algorithms Computational geometry Convexity Iterative algorithms Kernel Kernels Machine learning max-margin classification multiple kernel learning Optimization Pattern analysis Signal processing algorithms
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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