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Multi-embedding space set-kernel and its application to multi-instance learning
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DOI：10.1016/j.neucom.2022.09.067.png)
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Abstract
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Set-level problems become critical when we are interested in animals in pictures, links in web pages, and components in drugs. The key issue is to measure the similarity between two sets. This paper develops a data-dependent multi-embedding space set-kernel (MSK) with close to linear time complexity and applies it to multi-instance learning (MIL), which is a typical set-level problem. The majority of current set-kernels are independent of the underlying data distribution. In contrast, MSK indirectly measures set similarity by determining the relationship between embedding vectors. Each set's embedding vectors are new representations with controlled dimensionality in the multi-embedding space. Multi-embedding space is described here as a set containing multiple subspaces based on the distribution of the data set. In addition, the MSK feature map is used to speed up the computation of similarity over the entire data set. Extensive experiments were done on 46 MIL data sets across five application domains. The results demonstrate that MSK has the lowest average classification loss and the highest stability com-pared with the rival set-kernels. The linear time complexity is also verified. Source codes are available at https://github.com/InkiInki/MSK.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Multi-instance learning
Multi-embedding space
Set-kernel
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