We present a unified model of what was tradi- tionally viewed as two separate tasks: data asso- ciation and intensity tracking of multiple topics over time. In the data association part, the task is to assign a topic (a class) to each data point, and the intensity tracking part models the bursts and changes in intensities of topics over time. Our approach to this problem combines an extension of Factorial Hidden Markov models for topic intensity tracking with exponential order statis- tics for implicit data association. Experiments on text and email datasets show that the interplay of classification and topic intensity track- ing improves the accuracy of both classification and intensity tracking. Even a little noise in topic assignments can mislead the traditional algorithms. However, our approach detects correct topic intensities even with 30% topic noise.

@inproceedings{krause06data,
	author = {Andreas Krause and Jure Leskovec and Carlos Guestrin},
	booktitle = {International conference on Machine learning (ICML)},
	month = {June},
	title = {Data Association for Topic Intensity Tracking},
	year = {2006}}