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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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 42; číslo 6; s. 1303 - 1316
Hlavní autoři:	Liu, Xinwang, Wang, Lei, Zhu, Xinzhong, Li, Miaomiao, Zhu, En, Liu, Tongliang, Liu, Li, Dou, Yong, Yin, Jianping
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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