Kernel ridge regression for out-of-sample mapping in supervised manifold learning

• Published in: Expert systems with applications Vol. 39; no. 9; pp. 7757 - 7762
• Main Authors: Orsenigo, Carlotta, Vercellis, Carlo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2012
• Subjects: Agglomeration; Classification; Kernel ridge regression; Learning; Manifolds; Mapping; Nonlinear dimensionality reduction; Regression; Ridges; Supervised isomap; Supervised manifold learning; Supervised manifold learning; Kernel ridge regression; Supervised isomap; Classification; Nonlinear dimensionality reduction
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.01.060

► A novel supervised manifold learning method for classification is presented. ► It combines a supervised variant of Isomap with multi-output kernel ridge regression. ► Extensive computational tests show the effectiveness of the proposed technique. Manifold learning methods for unsupervised nonlinear dimensionality reduction have proven effective in the visualization of high dimensional data sets. When dealing with classification tasks, supervised extensions of manifold learning techniques, in which class labels are used to improve the embedding of the training points, require an appropriate method for out-of-sample mapping. In this paper we propose multi-output kernel ridge regression (KRR) for out-of-sample mapping in supervised manifold learning, in place of general regression neural networks (GRNN) that have been adopted by previous studies on the subject. Specifically, we consider a supervised agglomerative variant of Isomap and compare the performance of classification methods when the out-of-sample embedding is based on KRR and GRNN, respectively. Extensive computational experiments, using support vector machines and k-nearest neighbors as base classifiers, provide statistical evidence that out-of-sample mapping based on KRR consistently dominates its GRNN counterpart, and that supervised agglomerative Isomap with KRR achieves a higher accuracy than direct classification methods on most data sets.