Performance analysis of neural network topologies and hyperparameters for deep clustering
Muhammed Furkan Küçük, Ismail Uysal · 2020
Deep learning found its initial footing in supervised applications such as image and voice recognition successes of which were followed by deep generative models across similar domains. In recent years, researchers have proposed creative learning representations to utilize the unparalleled generalization capabilities of such structures for unsupervised applications commonly called deep clustering. This paper presents a comprehensive analysis of popular deep clustering architectures including deep autoencoders and convolutional autoencoders to study how network topology, hyperparameters and clustering coefficients impact accuracy. Three popular benchmark datasets are used including MNIST, CIFAR10 and SVHN to ensure data independent results. In total, 20 different pairings of topologies and clustering coefficients are used for both the standard and convolutional autoencoder architectures across three different datasets for a joint analysis of 120 unique combinations with sufficient repetitive testing for statistical significance. The results suggest that there is a general optimum when it comes to choosing the coding layer (latent dimension) size which is correlated to an extent with the complexity of the dataset. Moreover, for image datasets, when color makes a meaningful contribution to the identity of the observation, it also helps improve the subsequent deep clustering performance.