Hierarchical Nested CRFs for Segmentation and Labeling of Physiological Time Series

Citation:
Adams, R. J., E. Thomaz, and B. M. Marlin, "Hierarchical Nested CRFs for Segmentation and Labeling of Physiological Time Series", NIPS Workshop on Machine Learning in Healthcare, 2015.

Date Presented:

2015

Abstract:

In this paper, we address the problem of nested hierarchical segmentation
and labeling of time series data. We present a hierarchical
span-based conditional random field framework for this problem that
leverages higher-order factors to enforce the nesting constraints. The framework can
incorporate a variety of additional factors including higher order cardinality
factors. This research is motivated by hierarchical activity recognition problems
in the field of mobile Health (mHealth). We show that the specific model of interest in the mHealth setting supports exact MAP inference in quadratic time. Learning is accomplished in the structured support vector machine framework. We show positive results on real and synthetic data sets.

Notes:

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