scholarly journals Unsupervised visual perception-based representation learning for time-series and trajectories

2021 ◽  
Author(s):  
Gaurangi Anand
Keyword(s):  
Time Series ◽  
Visual Perception ◽  
Representation Learning

A deep multi-task representation learning method for time series classification and retrieval

2021 ◽  
Vol 555 ◽  
pp. 17-32
Author(s):  
Ling Chen ◽  
Donghui Chen ◽  
Fan Yang ◽  
Jianling Sun
Keyword(s):  
Time Series ◽  
Representation Learning ◽  
Learning Method ◽  
Time Series Classification ◽  
Task Representation

scholarly journals Adversarial Unsupervised Representation Learning for Activity Time-Series

2019 ◽  
Vol 33 ◽  
pp. 834-841 ◽  
Author(s):  
Karan Aggarwal ◽  
Shafiq Joty ◽  
Luis Fernandez-Luque ◽  
Jaideep Srivastava
Keyword(s):  
Time Series ◽  
Chronic Conditions ◽  
Empirical Evaluation ◽  
Representation Learning ◽  
Time Segment ◽  
Linear Classifiers ◽  
Daily Routines ◽  
Learning Technique ◽  
Prevention And Cure ◽  
Better Than

Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices pro-vides a significant new source, making it possible to track the user’s lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activ-ity2vecthat learns and “summarizes” the discrete-valued ac-tivity time-series. It learns the representations with three com-ponents: (i) the co-occurrence and magnitude of the activ-ity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with ad-versarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routines.

Convolutional Autoencoder for Unsupervised Representation Learning of PolSAR Time-Series

2021 ◽  
Author(s):  
Thomas Di Martino ◽  
Regis Guinvarc'h ◽  
Laetitia Thirion-Lefevre ◽  
Elise Colin Koeniguer
Keyword(s):  
Time Series ◽  
Representation Learning ◽  
Convolutional Autoencoder

Representation learning for unsupervised heterogeneous multivariate time series segmentation and its application

2019 ◽  
Vol 130 ◽  
pp. 272-281 ◽  
Author(s):  
Hyunjoong Kim ◽  
Han Kyul Kim ◽  
Misuk Kim ◽  
Jooseoung Park ◽  
Sungzoon Cho ◽  
...  
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Representation Learning ◽  
Time Series Segmentation

Temporal representation learning for time series classification

2020 ◽  
Author(s):  
Yupeng Hu ◽  
Peng Zhan ◽  
Yang Xu ◽  
Jia Zhao ◽  
Yujun Li ◽  
...  
Keyword(s):  
Time Series ◽  
Representation Learning ◽  
Time Series Classification ◽  
Temporal Representation

scholarly journals Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach

2022 ◽  
Vol 3 (1) ◽  
pp. 1-26
Author(s):  
Omid Hajihassani ◽  
Omid Ardakanian ◽  
Hamzeh Khazaei
Keyword(s):  
Time Series ◽  
Linear Transformation ◽  
Input Data ◽  
Time Series Data ◽  
Representation Learning ◽  
Sensor Data ◽  
Series Data ◽  
Sensitive Information ◽  
Latent Space ◽  
Low Dimensional

The abundance of data collected by sensors in Internet of Things devices and the success of deep neural networks in uncovering hidden patterns in time series data have led to mounting privacy concerns. This is because private and sensitive information can be potentially learned from sensor data by applications that have access to this data. In this article, we aim to examine the tradeoff between utility and privacy loss by learning low-dimensional representations that are useful for data obfuscation. We propose deterministic and probabilistic transformations in the latent space of a variational autoencoder to synthesize time series data such that intrusive inferences are prevented while desired inferences can still be made with sufficient accuracy. In the deterministic case, we use a linear transformation to move the representation of input data in the latent space such that the reconstructed data is likely to have the same public attribute but a different private attribute than the original input data. In the probabilistic case, we apply the linear transformation to the latent representation of input data with some probability. We compare our technique with autoencoder-based anonymization techniques and additionally show that it can anonymize data in real time on resource-constrained edge devices.

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