Paper Info
Title
Ramp-loss nonparallel support vector regression: Robust, sparse and scalable approximation.
Abstract
Although the twin support vector regression (TSVR) has been extensively studied and diverse variants are successfully developed, when it comes to outlier-involved training set, the regression model can be wrongly driven towards the outlier points, yielding extremely poor generalization performance. To overcome such shortcoming, a Ramp-loss nonparallel support vector regression (RL-NPSVR) is proposed in this work. By adopting Ramp ε-insensitive loss function and another Ramp-type linear loss function, RL-NPSVR can not only explicitly filter noise and outlier suppression but also have an excellent sparseness. The non- convexity of RL-NPSVR is solved by concave–convex programming (CCCP). Because a regularized term is added into each primal problem by rigidly following the structural risk minimization (SRM) principle, CCCP actually solves a series of reconstructed convex optimizations which have the same formulation of dual problem as the standard SVR, so that computing inverse matrix is avoided and SMO-type fast algorithm can be used to accelerate the training process. Numerical experiments on various datasets have verified the effectiveness of our proposed RL-NPSVR in terms of outlier sensitivity, generalization ability, sparseness and scalability.
Year
DOI
Venue
2018
10.1016/j.knosys.2018.02.016
Knowledge-Based Systems
Keywords
Field
DocType
Support vector regression,Twin support vector regression,Ramp loss,CCCP,Sparseness
Data mining,Convexity,Regression analysis,Computer science,Matrix (mathematics),Support vector machine,Outlier,Algorithm,Duality (optimization),Structural risk minimization,Scalability
Journal
Volume
ISSN
Citations 
147
0950-7051
2
PageRank 
References 
Authors
0.36
34
4
Name
Order
Citations
PageRank
Long Tang171.44
Ying-Jie Tian2186.34
Chunyan Yang341.74
Panos M. Pardalos414119.60