Clustering With Orthogonal AutoEncoder

Recently, clustering algorithms based on deep AutoEncoder attract lots of attention due to their excellent clustering performance. On the other hand, the success of PCA-Kmeans and spectral clustering corroborates that the orthogonality of embedding is beneficial to increase the clustering accuracy. In this paper, we propose a novel dimensional reduction model, called Orthogonal AutoEncoder (OAE), which encourages the orthogonality of the learned embedding. Furthermore, we propose a joint deep Clustering framework based on Orthogonal AutoEncoder (COAE), and this new framework is capable of extracting the latent embedding and predicting the clustering assignment simultaneously. The COAE stacks a fully connected clustering layer on top of the OAE, where the activation function of the clustering layer is the multinomial logistic regression function. The loss function of the COAE contains two terms: the reconstruction loss and the clustering-oriented loss. The first one is a data-dependent term in order to prevent overfitting. The other one is the cross entropy between the predicted assignment and the auxiliary target distribution. The network parameters of the COAE can be effectively updated by the mini-batch stochastic gradient descent algorithm and the back-propagation approach. The experiments on benchmark datasets empirically demonstrate that the COAE can achieve superior or competitive clustering performance as state-of-the-art deep clustering frameworks. The implementation of our algorithm is available at https://github.com/WangDavey/COAE