Semi-supervised local multi-manifold Isomap by linear embedding for feature extraction. (April 2018)

• Record Type: Journal Article
• Authors: Zhang, Yan; Zhang, Zhao; Qin, Jie; Zhang, Li; Li, Bing; Li, Fanzhang
• Is Part Of: Pattern recognition. Volume 76(2018:Apr.)
• Page Start: 662
• Page End: 678
• Publication Date: 2018-04
• Subjects: Semi-supervised manifold feature extraction -- Local multi-manifold Isomap -- Linear embedding -- Classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.09.043
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We explore the discriminative feature extraction problem. A Semi‐Supervised local multi‐manifold Isomap by linear embedding is proposed. Our model can use labeled and unlabeled data to deliver manifold features. Our model aims to minimize pairwise intra‐class distances in the same manifold. Our model aims to maximize the distances between different manifolds. Abstract: In this paper, we mainly propose a semi-supervised local multi-manifold Isomap learning framework by linear embedding, termed SSMM-Isomap, that can apply the labeled and unlabeled training samples to perform the joint learning of neighborhood preserving local nonlinear manifold features and a linear feature extractor. The formulation of SSMM-Isomap aims at minimizing pairwise distances of intra-class points in the same manifold and maximizing the distances over different manifolds. To enhance the performance of nonlinear manifold feature learning, we also incorporate the neighborhood reconstruction error to preserve local topology structures between both labeled and unlabeled samples. To enable our SSMM-Isomap to extract local manifold features from the outside new data, we also add a feature approximation error that correlates manifold features with embedded features by the jointly learnt feature extractor. Thus, the learnt linear extractor can extract the local manifold features from the new data efficiently by direct embedding. To optimize the proposed objective function, two effective schemes are presented, i.e., Scaling by MAjorizing a Complicated Function and Eigen-decomposition. Notice that the comparison of the proposed two solvers is also described. We mainly evaluate SSMM-Isomap for manifold feature learning, data clustering and classification. Extensive simulation results verify the effectiveness of our SSMM-Isomap algorithm, compared with other related feature learning techniques.