Probabilistic models for multi-view semi-supervised learning and coding

[摘要] (cont.) In contrast we develop co-training algorithms that are able to learn from noisy sensor data corrupted by complex per-sample noise processes, e.g., occlusion common to multi sensor classification problems. We propose a probabilistic heteroscedastic approach to co-training that simultaneously discovers the amount of noise on a per-sample basis, while solving the classification task. This results in accurate performance in the presence of occlusion or other complex noise processes. We also investigate an extension of this idea for supervised multi-view learning where we develop a Bayesian multiple kernel learning algorithm that can learn a local weighting over each view of the input space. We additionally consider the problem of distributed object recognition or indexing from multiple cameras, where the computational power available at each camera sensor is limited and communication between cameras is prohibitively expensive. In this scenario, it is desirable to avoid sending redundant visual features from multiple views. Traditional supervised feature selection approaches are inapplicable as the class label is unknown at each camera. In this thesis, we propose an unsupervised multi-view feature selection algorithm based on a distributed coding approach. With our method, a Gaussian Process model of the joint view statistics is used at the receiver to obtain a joint encoding of the views without directly sharing information across encoders. We demonstrate our approach on recognition and indexing tasks with multi-view image databases and show that our method compares favorably to an independent encoding of the features from each camera.

[发布机构] Massachusetts Institute of Technology