Semi-supervised multiple kernel intact discriminant space learning for image recognition

In practice, huge amount of samples is readily available, while labeled samples are often very limited and too expensive to be easily obtained. Multi-view features usually reveal different types of traits of labeled and unlabeled samples. Semi-supervised multi-view learning is a learning paradigm designed to meet the requirement of learning from complementary information of multiple views of labeled and unlabeled samples. In this paper, we propose a semi-supervised multiple kernel intact discriminant space learning (SMKIDSL) method to discover latent intact feature representations for those samples. SMKIDSL employs correlation discriminant analysis and label regression to fully use class label information for enhancing the discriminant power of latent intact feature representations. In SMKIDSL, multi-view collaboration learning mechanism is utilized to efficiently integrate complementary information of multiple views, which enables optimal view being dominant in learning process. Besides, kernel technique is used to tackle nonlinear issue of original multi-view features for exploiting more discriminant information. Comprehensive experiments are conducted on Caltech 101, LFW, MNIST and RGB-D datasets. And the experimental results demonstrate the effectiveness and efficiency of our proposed method. The robustness of our method is also confirmed by those results.