Nonpeaked Discriminant Analysis for Data Representation.

Clicks: 332
ID: 66013
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #21 of 56 articles by views in IEEE Transactions on Neural Networks and Learning Systems

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Of late, there are many studies on the robust discriminant analysis, which adopt L₁-norm as the distance metric, but their results are not robust enough to gain universal acceptance. To overcome this problem, the authors of this article present a nonpeaked discriminant analysis (NPDA) technique, in which cutting L₁-norm is adopted as the distance metric. As this kind of norm can better eliminate heavy outliers in learning models, the proposed algorithm is expected to be stronger in performing feature extraction tasks for data representation than the existing robust discriminant analysis techniques, which are based on the L₁-norm distance metric. The authors also present a comprehensive analysis to show that cutting L₁-norm distance can be computed equally well, using the difference between two special convex functions. Against this background, an efficient iterative algorithm is designed for the optimization of the proposed objective. Theoretical proofs on the convergence of the algorithm are also presented. Theoretical insights and effectiveness of the proposed method are validated by experimental tests on several real data sets.
Reference Key
ye2019nonpeakedieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ye, Qiaolin;Li, Zechao;Fu, Liyong;Zhang, Zhao;Yang, Wankou;Yang, Guowei;
Journal IEEE Transactions on Neural Networks and Learning Systems
Year 2019
DOI
10.1109/TNNLS.2019.2944869
URL
Keywords
Deep learning pig mask r-cnn kernel-extreme learning machine mounting behaviour

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.