Deep Transductive Semi-supervised Maximum Margin Clustering
This addresses the challenge of learning better representations for clustering when data lie on non-linear manifolds, which is important for machine learning and computer vision applications, though it appears incremental as it builds on existing deep learning and maximum margin frameworks.
The paper tackles the problem of semi-supervised clustering on non-linear manifolds by proposing a deep transductive approach that unifies feature learning, maximum margin techniques, and transductive learning, resulting in significantly better performance than state-of-the-art methods.
Semi-supervised clustering is an very important topic in machine learning and computer vision. The key challenge of this problem is how to learn a metric, such that the instances sharing the same label are more likely close to each other on the embedded space. However, little attention has been paid to learn better representations when the data lie on non-linear manifold. Fortunately, deep learning has led to great success on feature learning recently. Inspired by the advances of deep learning, we propose a deep transductive semi-supervised maximum margin clustering approach. More specifically, given pairwise constraints, we exploit both labeled and unlabeled data to learn a non-linear mapping under maximum margin framework for clustering analysis. Thus, our model unifies transductive learning, feature learning and maximum margin techniques in the semi-supervised clustering framework. We pretrain the deep network structure with restricted Boltzmann machines (RBMs) layer by layer greedily, and optimize our objective function with gradient descent. By checking the most violated constraints, our approach updates the model parameters through error backpropagation, in which deep features are learned automatically. The experimental results shows that our model is significantly better than the state of the art on semi-supervised clustering.