Metric Learning for comparison of HMMs using Graph Neural Networks

Rajan Kumar Soni, Karthick Seshadri, Balaraman Ravindran
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:1365-1380, 2021.

Abstract

Hidden Markov models (HMMs) belong to the class of double embedded stochastic models which were originally leveraged for speech recognition and synthesis. HMMs subsequently became a generic sequence model across multiple domains like NLP, bio-informatics and thermodynamics to name a few. Literature has several heuristic metrics to compare two HMMs by factoring in their structure and emission probability distributions in HMM nodes. However, typical structure-based metrics overlook the similarity between HMMs having different structures yet similar behavior and typical behavior-based metrics rely on the representativeness of the reference sequence used for assessing the similarity in behavior. Further, little exploration has taken place in leveraging the recent advancements in deep graph neural networks for learning effective representations for HMMs. In this paper, we propose two novel deep neural network based approaches to learn embeddings for HMMs and evaluate the validity of the embeddings based on subsequent clustering and classification tasks. Our proposed approaches use a Graph variational Autoencoder and diffpooling based Graph neural network (GNN) to learn embeddings for HMMs. The graph autoencoder infers latent low-dimensional flat embeddings for HMMs in a task-agnostic manner; whereas the diffpooling based graph neural network learns class-label aware embeddings by inferring and aggregating a hierarchical set of clusters and sub-clusters of graph nodes. Empirical results reveal that the HMM embeddings learnt through the Graph variational autoencoders and diffpooling based GNN outperform the popular heuristics as measured by the cluster quality metrics and the classification accuracy in downstream tasks.

Cite this Paper


BibTeX
@InProceedings{pmlr-v157-soni21a, title = {Metric Learning for comparison of HMMs using Graph Neural Networks}, author = {Soni, Rajan Kumar and Seshadri, Karthick and Ravindran, Balaraman}, booktitle = {Proceedings of The 13th Asian Conference on Machine Learning}, pages = {1365--1380}, year = {2021}, editor = {Balasubramanian, Vineeth N. and Tsang, Ivor}, volume = {157}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v157/soni21a/soni21a.pdf}, url = {https://proceedings.mlr.press/v157/soni21a.html}, abstract = {Hidden Markov models (HMMs) belong to the class of double embedded stochastic models which were originally leveraged for speech recognition and synthesis. HMMs subsequently became a generic sequence model across multiple domains like NLP, bio-informatics and thermodynamics to name a few. Literature has several heuristic metrics to compare two HMMs by factoring in their structure and emission probability distributions in HMM nodes. However, typical structure-based metrics overlook the similarity between HMMs having different structures yet similar behavior and typical behavior-based metrics rely on the representativeness of the reference sequence used for assessing the similarity in behavior. Further, little exploration has taken place in leveraging the recent advancements in deep graph neural networks for learning effective representations for HMMs. In this paper, we propose two novel deep neural network based approaches to learn embeddings for HMMs and evaluate the validity of the embeddings based on subsequent clustering and classification tasks. Our proposed approaches use a Graph variational Autoencoder and diffpooling based Graph neural network (GNN) to learn embeddings for HMMs. The graph autoencoder infers latent low-dimensional flat embeddings for HMMs in a task-agnostic manner; whereas the diffpooling based graph neural network learns class-label aware embeddings by inferring and aggregating a hierarchical set of clusters and sub-clusters of graph nodes. Empirical results reveal that the HMM embeddings learnt through the Graph variational autoencoders and diffpooling based GNN outperform the popular heuristics as measured by the cluster quality metrics and the classification accuracy in downstream tasks.} }
Endnote
%0 Conference Paper %T Metric Learning for comparison of HMMs using Graph Neural Networks %A Rajan Kumar Soni %A Karthick Seshadri %A Balaraman Ravindran %B Proceedings of The 13th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Vineeth N. Balasubramanian %E Ivor Tsang %F pmlr-v157-soni21a %I PMLR %P 1365--1380 %U https://proceedings.mlr.press/v157/soni21a.html %V 157 %X Hidden Markov models (HMMs) belong to the class of double embedded stochastic models which were originally leveraged for speech recognition and synthesis. HMMs subsequently became a generic sequence model across multiple domains like NLP, bio-informatics and thermodynamics to name a few. Literature has several heuristic metrics to compare two HMMs by factoring in their structure and emission probability distributions in HMM nodes. However, typical structure-based metrics overlook the similarity between HMMs having different structures yet similar behavior and typical behavior-based metrics rely on the representativeness of the reference sequence used for assessing the similarity in behavior. Further, little exploration has taken place in leveraging the recent advancements in deep graph neural networks for learning effective representations for HMMs. In this paper, we propose two novel deep neural network based approaches to learn embeddings for HMMs and evaluate the validity of the embeddings based on subsequent clustering and classification tasks. Our proposed approaches use a Graph variational Autoencoder and diffpooling based Graph neural network (GNN) to learn embeddings for HMMs. The graph autoencoder infers latent low-dimensional flat embeddings for HMMs in a task-agnostic manner; whereas the diffpooling based graph neural network learns class-label aware embeddings by inferring and aggregating a hierarchical set of clusters and sub-clusters of graph nodes. Empirical results reveal that the HMM embeddings learnt through the Graph variational autoencoders and diffpooling based GNN outperform the popular heuristics as measured by the cluster quality metrics and the classification accuracy in downstream tasks.
APA
Soni, R.K., Seshadri, K. & Ravindran, B.. (2021). Metric Learning for comparison of HMMs using Graph Neural Networks. Proceedings of The 13th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 157:1365-1380 Available from https://proceedings.mlr.press/v157/soni21a.html.

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