Author name cluster

Leonard Poon

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2017 Conference Paper

Latent Tree Analysis

Nevin Zhang
Leonard Poon

Latent tree analysis seeks to model the correlations amonga set of random variables using a tree of latent variables. It was proposed as an improvement to latent class analysisÑa method widely used in social sciences and medicine to identify homogeneous subgroups in a population. It provides new and fruitful perspectives on a number of machine learningareas, including cluster analysis, topic detection, and deep probabilistic modeling. This paper gives an overview of the research on latent tree analysis and various ways it is used inpractice.

PDF Details

AAAI Conference 2016 Conference Paper

Progressive EM for Latent Tree Models and Hierarchical Topic Detection

Peixian Chen
Nevin Zhang
Leonard Poon
Zhourong Chen

Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic deﬁnition, topic-document relationship, and learning method. It has been shown to discover signiﬁcantly more coherent topics and better topic hierarchies. However, HLTA relies on the Expectation-Maximization (EM) algorithm for parameter estimation and hence is not efﬁcient enough to deal with large datasets. In this paper, we propose a method to drastically speed up HLTA using a technique inspired by the advances in the method of moments. Empirical experiments show that our method greatly improves the efﬁciency of HLTA. It is as efﬁcient as the state-of-the-art LDA-based method for hierarchical topic detection and ﬁnds substantially better topics and topic hierarchies.

PDF Details

AAAI Conference 2015 Conference Paper

Unidimensional Clustering of Discrete Data Using Latent Tree Models

April Liu
Leonard Poon
Nevin Zhang

This paper is concerned with model-based clustering of discrete data. Latent class models (LCMs) are usually used for the task. An LCM consists of a latent variable and a number of attributes. It makes the overly restrictive assumption that the attributes are mutually independent given the latent variable. We propose a novel method to relax the assumption. The key idea is to partition the attributes into groups such that correlations among the attributes in each group can be properly modeled by using one single latent variable. The latent variables for the attribute groups are then used to build a number of models and one of them is chosen to produce the clustering results. Extensive empirical studies have been conducted to compare the new method with LCM and several other methods (K-means, kernel Kmeans and spectral clustering) that are not model-based. The new method outperforms the alternative methods in most cases and the differences are often large.

PDF Details