Dissimilarity Mixture Autoencoder for Deep Clustering
This work addresses clustering challenges in domains like image and text analysis, but it appears incremental as it extends classical methods with neural network integration.
The authors tackled the problem of feature-based clustering by proposing the dissimilarity mixture autoencoder (DMAE), which integrates a flexible dissimilarity function into deep learning architectures, and experimental results showed it is competitive in unsupervised classification accuracy and normalized mutual information on benchmark datasets.
The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture. It internally represents a dissimilarity mixture model (DMM) that extends classical methods like K-Means, Gaussian mixture models, or Bregman clustering to any convex and differentiable dissimilarity function through the reinterpretation of probabilities as neural network representations. DMAE can be integrated with deep learning architectures into end-to-end models, allowing the simultaneous estimation of the clustering and neural network's parameters. Experimental evaluation was performed on image and text clustering benchmark datasets showing that DMAE is competitive in terms of unsupervised classification accuracy and normalized mutual information. The source code with the implementation of DMAE is publicly available at: https://github.com/juselara1/dmae