Multi-view dimensionality reduction based on Universum learning

Flexible Multi-View Unsupervised Graph Embedding

Faced with the increasing data diversity and dimensionality, multi-view dimensionality reduction has been an important technique in computer vision, data mining and multi-media applications. Since collecting labeled data is difficult and costly, unsupervised learning is of great significance. Generally, it is crucial to explore the complementarity or independence of different feature spaces in multi-view learning. How to find a low-dimensional subspace to preserve the intrinsic structure of original unlabeled high-dimensional multi-view data is still challenging. In addition, noises and outliers always appear in real data. In this study, we propose a novel model called flexible multi-view unsupervised graph embedding (FMUGE). A flexible regression residual term is introduced so that the strict linear mapping is relaxed, new-coming data and noises are better handled, and the raw data negotiate with the learned low-dimensional representation in the procedure. To ensure the consistency among multiple views, FMUGE adaptively weights different features and fuses them to get an optimal multi-view consensus similarity graph, which assists high-quality graph embedding. We propose an efficient alternating iterative algorithm to optimize the proposed model. Finally, experimental results on synthetic and benchmark datasets show the significant improvement of FMUGE over the state-of-the-art methods.

Improved multi-view privileged support vector machine

Multi-view learning (MVL) concentrates on the problem of learning from the data represented by multiple distinct feature sets. The consensus and complementarity principles play key roles in multi-view modeling. By exploiting the consensus principle or the complementarity principle among different views, various successful support vector machine (SVM)-based multi-view learning models have been proposed for performance improvement. Recently, a framework of learning using privileged information (LUPI) has been proposed to model data with complementary information. By bridging connections between the LUPI paradigm and multi-view learning, we have presented a privileged SVM-based two-view classification model, named PSVM-2V, satisfying both principles simultaneously. However, it can be further improved in these three aspects: (1) fully unleash the power of the complementary information among different views; (2) extend to multi-view case; (3) construct a more efficient optimization solver. Therefore, in this paper, we propose an improved privileged SVM-based model for multi-view learning, termed as IPSVM-MV. It directly follows the standard LUPI model to fully utilize the multi-view complementary information; also it is a general model for multi-view scenario, and an alternating direction method of multipliers (ADMM) is employed to solve the corresponding optimization problem efficiently. Further more, we theoretically analyze the performance of IPSVM-MV from the viewpoints of the consensus principle and the generalization error bound. Experimental results on 75 binary data sets demonstrate the effectiveness of the proposed method; here we mainly concentrate on two-view case to compare with state-of-the-art methods.