Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection
Author:
Affiliation:
1. CITEC Cluster of Excellence, Bielefeld University, Bielefeld, Germany
Funder
SIGIR Student Travel Grant
German Research Foundation (DFG)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3357384.3357865
Reference32 articles.
1. $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
2. Multiple kernel learning, conic duality, and the SMO algorithm
3. Prototype selection for interpretable classification
4. A Kernel for Time Series Based on Global Alignments
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