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Localized multiple kernel learning using graph modularity
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DOI：10.1016/j.patrec.2022.01.018.png)
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Multiple kernel learning (MKL) algorithms exploit information from multiple feature representations by assigning weights to each representation in the kernel space, and later combining them. However, this ignores the fact that data points exhibit locally varying characteristics. To address this problem, localized MKL algorithms learn locality-specific kernel weights which determine each base kernel's influence in the locality under consideration. Here, we relate the problem of determining the relevance of base kernels for classification to that of quantifying community structure in graphs. Next, we derive sample-specific kernel weights using graph modularity. Through experiments on publicly available datasets, we show that the proposed approach offers a viable alternative to state-of-the-art MKL approaches while being computationally inexpensive. (C) 2022 Elsevier B.V. All rights reserved.
Keyword:
Multiple kernel combining
Multi-view data
SVM
Heuristic approach
Graph modularity
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
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Methods for the combination of kernel matrices within a support vector framework.png)
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