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A bi-level formulation for multiple kernel learning via self-paced training
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AI
DOI：10.1016/j.patcog.2022.108770.png)
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Abstract
En 中文
Multiple kernel learning (MKL) is a crucial issue which has been widely researched over the last two decades. Although existing MKL algorithms have achieved satisfactory performance in a broad range of applications, these methods do not adequately consider the adverse effects of unreliable or less reli-able instances. To handle this shortcoming, we formulate multiple kernel learning in a bi-level learning paradigm consisting of the kernel combination weight learning (KWL) stage and the self-paced learning (SPL) stage, which alternatively negotiate with each other. The KWL stage dynamically absorbs reliable instances into model learning to accurately capture neighborhood relationships and obtains kernel co-efficients via maximizing both global and local kernel alignment in a common schema. The SPL stage automatically evaluates the reliability of training samples via self-paced training. The extensive exper-iments indicate the robustness and superiority of the presented approach in comparison with existing MKL methods.(c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Multiple kernel learning
Self-paced learning
Bi-level optimization
Local kernel alignment
Global kernel alignment