IDECF: Improved Deep Embedding Clustering With Deep Fuzzy Supervision
Mohammadreza Sadeghi, Narges Armanfard
Abstract
Deep clustering algorithms utilize a deep neural network to map data points in a lower-dimensional space which is more suitable for clustering task. Recent algorithms employ autoencoder to jointly learn a lower-dimensional space (aka latent space) and perform data clustering through minimizing a clustering loss. These algorithms suffer from the fact that the true cluster assignments are unknown because of the unsupervised nature of the task. Thus, they adopt a self-training strategy and estimate the true cluster labels using the algorithm parameters; while the true parameters’ value is unknown at the problem outset. To address this difficulty, we propose a deep clustering technique, called IDECF, whereby the true cluster assignments are estimated using an individual deep fully connected network (FCM-Net) which takes its input from the latent space of an autoencoder. The proposed IDECF is trained in an end-to-end manner by minimizing a linear combination of reconstruction loss and clustering loss. Experimental results on benchmark datasets demonstrate the viability and effectiveness of the proposed algorithm.