Collaborative multifeature fusion for transductive spectral learning.

Much existing work of multifeature learning relies on the agreement among different feature types to improve the clustering or classification performance. However, as different feature types could have different data characteristics, such a forced agreement among different feature types may not bring a satisfactory result. We propose a novel transductive learning approach that considers multiple feature types simultaneously to improve the classification performance. Instead of forcing different feature types to agree with each other, we perform spectral clustering in different feature types separately. Each data sample is then described by a co-occurrence of feature patterns among different feature types, and we apply these feature co-occurrence representations to perform transductive learning, such that data samples of similar feature co-occurrence pattern will share the same label. As the spectral clustering results in different feature types and the formed co-occurrence patterns influence each other under the transductive learning formulation, an iterative optimization approach is proposed to decouple these factors. Different from co-training that need to iteratively update individual feature type, our method allows all feature types to collaborate simultaneously. It can naturally handle multiple feature types together and is less sensitive to noisy feature types. The experimental results on synthetic, object, and action recognition datasets all validate the advantages of our method compared to state-of-the-art methods.