Deep soft clustering: simultaneous deep embedding and soft-partition clustering

Traditional clustering methods are not very effective when dealing with high-dimensional and huge datasets. Even if there are some traditional dimensionality reduction methods such as Principal components analysis (PCA), Linear discriminant analysis (LDA) and T-distributed stochastic neighbor embedding (T-SNE), they still can not significantly improve the effect of the clustering algorithm in this scenario. Recent studies have combined Non-linear dimensionality reduction achieved by deep neural networks with hard-partition clustering, and have achieved reliability results, but these methods can not update the parameters of dimensionality reduction and clustering at the same time. We found that soft-partition clustering can be well combined with deep embedding, and the membership of Fuzzy c-means (FCM) can solve the problem that gradient descent can not be implemented because the assignment process of the hard-partition clustering algorithm is discrete, so that the algorithm can update the parameters of deep neural network (DNN) and cluster centroids at the same time. We build an continuous objective function that combine the soft-partition clustering with deep embedding, so that the learning representations can be cluster-friendly. The experimental results show that our proposed method of simultaneously optimizing the parameters of deep dimensionality reduction and clustering is better than the method with separate optimization.