Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4958210 | Computer Methods and Programs in Biomedicine | 2017 | 21 Pages |
Abstract
Conclusions: The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed â2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature.
Related Topics
Physical Sciences and Engineering
Computer Science
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Authors
Peng Cao, Xiaoli Liu, Jian Zhang, Wei Li, Dazhe Zhao, Min Huang, Osmar Zaiane,