Iterative Multiview Subspace Learning for Unpaired Multiview Clustering.

In real applications, several unpredictable or uncertain factors could result in unpaired multiview data, i.e., the observed samples between views cannot be matched.

Multi-output parameter-insensitive kernel twin SVR model.

Multi-output regression aims at mapping a multivariate input feature space to a multivariate output space. Currently, it is effective to extend the traditional support vector regression (SVR) mechanism to solve the multi-output case. However, some methods adopting a combination of single-output SVR models exhibit the severe drawback of not considering the possible correlations between outputs, and other multi-output SVRs show high computational complexity and are typically sensitive to parameters due to the influence of noise.

Learning Latent Low-Rank and Sparse Embedding for Robust Image Feature Extraction.

To defy the curse of dimensionality, the inputs are always projected from the original high-dimensional space into the target low-dimension space for feature extraction. However, due to the existence of noise and outliers, the feature extraction task for corrupted data is still a challenging problem. Recently, a robust method called low rank embedding (LRE) was proposed.