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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 7: AAAI-22 Technical Tracks 7

I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding

February 1, 2023

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Authors

Sirisha Rambhatla

University of Waterloo


Zhengping Che

Midea Group


Yan Liu

University of Southern California


DOI:

10.1609/aaai.v36i7.20776


Abstract:

Learning effective embeddings for potentially irregularly sampled time-series, evolving at different time scales, is fundamental for machine learning tasks such as classification and clustering. Task-dependent embeddings rely on similarities between data samples to learn effective geometries. However, many popular time-series similarity measures are not valid distance metrics, and as a result they do not reliably capture the intricate relationships between the multi-variate time-series data samples for learning effective embeddings. One of the primary ways to formulate an accurate distance metric is by forming distance estimates via Monte-Carlo-based expectation evaluations. However, the high-dimensionality of the underlying distribution, and the inability to sample from it, pose significant challenges. To this end, we develop an Importance Sampling based distance metric -- I-SEA -- which enjoys the properties of a metric while consistently achieving superior performance for machine learning tasks such as classification and representation learning. I-SEA leverages Importance Sampling and Non-parametric Density Estimation to adaptively estimate distances, enabling implicit estimation from the underlying high-dimensional distribution, resulting in improved accuracy and reduced variance. We theoretically establish the properties of I-SEA and demonstrate its capabilities via experimental evaluations on real-world healthcare datasets.

Topics: AAAI

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HOW TO CITE:

Sirisha Rambhatla||Zhengping Che||Yan Liu I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding Proceedings of the AAAI Conference on Artificial Intelligence (2022) 8045-8053.

Sirisha Rambhatla||Zhengping Che||Yan Liu I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding AAAI 2022, 8045-8053.

Sirisha Rambhatla||Zhengping Che||Yan Liu (2022). I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding. Proceedings of the AAAI Conference on Artificial Intelligence, 8045-8053.

Sirisha Rambhatla||Zhengping Che||Yan Liu. I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.8045-8053.

Sirisha Rambhatla||Zhengping Che||Yan Liu. 2022. I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding. "Proceedings of the AAAI Conference on Artificial Intelligence". 8045-8053.

Sirisha Rambhatla||Zhengping Che||Yan Liu. (2022) "I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding", Proceedings of the AAAI Conference on Artificial Intelligence, p.8045-8053

Sirisha Rambhatla||Zhengping Che||Yan Liu, "I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding", AAAI, p.8045-8053, 2022.

Sirisha Rambhatla||Zhengping Che||Yan Liu. "I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.8045-8053.

Sirisha Rambhatla||Zhengping Che||Yan Liu. "I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 8045-8053.

Sirisha Rambhatla||Zhengping Che||Yan Liu. I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding. AAAI[Internet]. 2022[cited 2023]; 8045-8053.


ISSN: 2374-3468


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