Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning

Hosseini B, Hammer B (2019)
Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest.

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Abstract / Bemerkung
Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has been used successfully for feature selection and finding the significant modalities of the data. In such applications, each base kernel represents one dimension of the data or is derived from one specific descriptor. Therefore, MKL finds an optimal weighting scheme for the given kernels to increase the classification accuracy. Nevertheless, the majority of the works in this area focus on only binary classification problems or aim for linear separation of the classes in the kernel space, which are not realistic assumptions for many real-world problems. In this paper, we propose a novel multi-class MKL framework which improves the state-of-the-art (SotA) by enhancing the local separation of the classes in the feature space. Besides, by using a sparsity term, our large-margin multiple kernel algorithm (LMMK) performs discriminative feature selection by aiming to employ a small subset of the base kernels. Based on our empirical evaluations on different real-world datasets, LMMK provides a competitive classification accuracy compared with the SotA algorithms in MKL. Additionally, it learns a sparse set of non-zero kernel weights which leads to a more interpretable feature selection and representation learning.
Stichworte
Multiple Kernel Learning; Feature Selection; Representation Learning; LMNN.
Erscheinungsjahr
2019
Konferenz
The 2019 International Joint Conference on Neural Networks (IJCNN)
Konferenzort
Budapest
Konferenzdatum
2019-07-14 – 2019-07-19
Page URI
https://pub.uni-bielefeld.de/record/2934192

Zitieren

Hosseini B, Hammer B. Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning. Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest.
Hosseini, B., & Hammer, B. (2019). Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning. Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest.
Hosseini, B., and Hammer, B. (2019).“Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning”. Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest.
Hosseini, B., & Hammer, B., 2019. Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning. Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest.
B. Hosseini and B. Hammer, “Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning”, Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, 2019.
Hosseini, B., Hammer, B.: Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning. Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest (2019).
Hosseini, Babak, and Hammer, Barbara. “Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning”. Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, 2019.

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arXiv: 1903.03364

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