Fig. 2

Method illustration using an autoencoder network. Clustering is applied to the data representation at each linear hidden layer. If there are K−1 linear hidden units, the data is projected into a subspace spanned by the top K−1 principal components. Consistency between the clusterings at each layer is maintained by enforcing a hierarchical constraint. a Graphical interpretation of an autoencoder network(s). b Corresponding hierarchical structure