Title
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
Abstract
We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. In general, our superstructure is a tree structure of multiple super latent variables and it is automatically learned from data. When there is only one latent variable in the superstructure, our model reduces to one that assumes the latent features to be generated from a Gaussian mixture model. We call our model the latent tree variational autoencoder (LTVAE). Whereas previous deep learning methods for clustering produce only one partition of data, LTVAE produces multiple partitions of data, each being given by one super latent variable. This is desirable because high dimensional data usually have many different natural facets and can be meaningfully partitioned in multiple ways.
Year
Venue
Field
2019
ICLR
Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,Machine learning
DocType
Citations 
PageRank 
Conference
1
0.35
References 
Authors
0
4
Name
Order
Citations
PageRank
Xiaopeng Li117132.15
Zhourong Chen222812.22
Leonard K. M. Poon39410.96
Nevin .L Zhang489597.21