Few-shot time series segmentation using prototype-defined infinite hidden Markov models

• URL: http://arxiv.org/abs/2102.03885v1
• Date: Sun, 7 Feb 2021 19:02:33 GMT
• Title: Few-shot time series segmentation using prototype-defined infinite hidden Markov models
• Authors: Yazan Qarout and Yordan P. Raykov and Max A. Little
• Abstract summary: We propose a framework for interpretable, few-shot analysis of non-stationary sequential data based on flexible graphical models. We show that RBF networks can be efficiently specified via prototypes allowing us to express complex nonstationary patterns. The utility of the framework is demonstrated on biomedical signal processing applications such as automated seizure detection from EEG data.
• Score: 3.527894538672585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a robust framework for interpretable, few-shot analysis of non-stationary sequential data based on flexible graphical models to express the structured distribution of sequential events, using prototype radial basis function (RBF) neural network emissions. A motivational link is demonstrated between prototypical neural network architectures for few-shot learning and the proposed RBF network infinite hidden Markov model (RBF-iHMM). We show that RBF networks can be efficiently specified via prototypes allowing us to express complex nonstationary patterns, while hidden Markov models are used to infer principled high-level Markov dynamics. The utility of the framework is demonstrated on biomedical signal processing applications such as automated seizure detection from EEG data where RBF networks achieve state-of-the-art performance using a fraction of the data needed to train long-short-term memory variational autoencoders.

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