Paper Info
Title
Heavy-Tailed Symmetric Stochastic Neighbor Embedding.
Abstract
Stochastic Neighbor Embedding (SNE) has shown to be quite promising for data visualization. Currently, the most popular implementation, t-SNE, is restricted to a particular Student t-distribution as its embedding distribution. Moreover, it uses a gradient descent algorithm that may require users to tune parameters such as the learning step size, momentum, etc., in finding its optimum. In this paper, we propose the Heavy-tailed Symmetric Stochastic Neighbor Embedding (HSSNE) method, which is a generalization of the t-SNE to accommodate various heavy-tailed embedding similarity functions. With this generalization, we are presented with two difficulties. The first is how to select the best embedding similarity among all heavy-tailed functions and the second is how to optimize the objective function once the heave-tailed function has been selected. Our contributions then are: (1) we point out that various heavy-tailed embedding similarities can be characterized by their negative score functions. Based on this finding, we present a parameterized subset of similarity functions for choosing the best tail-heaviness for HSSNE; (2) we present a fixed-point optimization algorithm that can be applied to all heavy-tailed functions and does not require the user to set any parameters; and (3) we present two empirical studies, one for unsupervised visualization showing that our optimization algorithm runs as fast and as good as the best known t-SNE implementation and the other for semi-supervised visualization showing quantitative superiority using the homogeneity measure as well as qualitative advantage in cluster separation over t-SNE.
Year
Venue
Field
2009
NIPS
Data visualization,Mathematical optimization,Gradient descent,Parameterized complexity,Homogeneity (statistics),Embedding,Computer science,Visualization,Artificial intelligence,Momentum,Empirical research,Machine learning
DocType
Citations 
PageRank 
Conference
19
0.94
References 
Authors
6
4
Name
Order
Citations
PageRank
Zhirong Yang128917.27
Irwin King26751325.94
Zenglin Xu392366.28
Erkki Oja46701797.08