# On Principal Curve-Based Classifiers and Similarity-Based Selective Sampling in Time-Series

arXiv: https://arxiv.org/abs/2204.04620 · HTML: https://mlsanity.com/paper/2204.04620/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 10 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [How to Construct Deep Recurrent Neural Networks](https://mlsanity.com/paper/1312.6026/) (2013)
- [Dropout improves Recurrent Neural Networks for Handwriting Recognition](https://mlsanity.com/paper/1312.4569/) (2013)
- [Insights into LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1902.10756/) (2019)
- [O-MedAL: Online Active Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1908.10508/) (2019)

## Abstract (arXiv)

Considering the concept of time-dilation, there exist some major issues with recurrent neural Architectures. Any variation in time spans between input data points causes performance attenuation in recurrent neural network architectures. Principal curve-based classifiers have the ability of handling any kind of variation in time spans. In other words, principal curve-based classifiers preserve the relativity of time while neural network architecture violates this property of time. On the other hand, considering the labeling costs and problems in online monitoring devices, there should be an algorithm that finds the data points which knowing their labels will cause in better performance of the classifier. Current selective sampling algorithms have lack of reliability due to the randomness of the proposed algorithms. This paper proposes a classifier and also a deterministic selective sampling algorithm with the same computational steps, both by use of principal curve as their building block in model definition.