mlsanityData to 14 Oct 2026

Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series

arXiv 2201.09907 ↗First posted 24 Jan 2022cs.LG stat.APcited by 0

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Abstract (arXiv)

In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i.e., during testing we can prescribe classes that are missing during training. This framework relies on two main components: (1) our newly proposed ordinal-quadruplet loss, which forces the model to learn latent representation while preserving the ordinal relation among labels, (2) testing procedure, which utilizes the property of latent representation (order preservation). We conduct experiments based on real world multivariate time series data and show the significant improvement in the prediction of missing labels even with 40% of the classes are missing from training. Compared with the well-known triplet loss optimization augmented with interpolation for missing information, in some cases, we nearly double the accuracy.

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References · 12

  1. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  2. 2017In Defense of the Triplet Loss for Person Re-Identificationcited by 786
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  7. 2018Processing of missing data by neural networkscited by 37
  8. 2019Deep Metric Learning Beyond Binary Supervisioncited by 36
  9. 2015Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functionscited by 33
  10. 2016Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Recordscited by 20
  11. 2019Spatio-Temporal Attention Pooling for Audio Scene Classificationcited by 6
  12. 2017Audio Scene Classification with Deep Recurrent Neural Networkscited by 2

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