Semi-unsupervised Learning: An In-depth Parameter Analysis

Davidson, Padraig; Buckermann, Florian; Steininger, Michael; Krause, Anna; Hotho, Andreas

doi:10.1007/978-3-030-87626-5_5

Cited by 1 publication

(8 citation statements)

References 14 publications

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“…This two-step process can be merged into one, by adapting the joint probability distribution 𝑝 Θ , resulting in a Gaussian Mixture Deep Generative model (GMM) capable of learning semi-supervised classification [16]. With some further modification we can use the inductive bias requirement to perform semi-unsupervised classification tasks with GMMs [7,29,30].…”

Section: Methodsmentioning

confidence: 99%

“…GMM for Semi-unsupervised Classification. In this work, we built on the work presented in [7,30] and adapt it to perform time series classification on raw sensor signals. That is, we are interested in the improved pattern recognition and performance shown in [7].…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we built on the work presented in [7,30] and adapt it to perform time series classification on raw sensor signals. That is, we are interested in the improved pattern recognition and performance shown in [7]. Therefore we adapt their work and replace the 2d convolutional networks for image classification, with 1d convolutions for raw time series.…”

Section: Methodsmentioning

confidence: 99%

“…Their approach uses extracted features from the time series and use them within the fully connected network. We are however interested in the use of the raw time series signals, and therefore focus on the convolutional approach presented in [7].…”

Section: Related Workmentioning

confidence: 99%

“…To combine the benefits of the high classification accuracy in semi-supervised models with the ability to detect new classes, the hybrid approach of semi-unsupervised learning [7,30] has emerged. In this paper we present SuSL4TS, a convolutional Gaussian mixture model for semi-unsupervised learning on time series data.…”

Section: Introductionmentioning

confidence: 99%

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Semi-unsupervised Learning for Time Series Classification

Davidson¹,

Steininger²,

Huhn³

et al. 2022

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Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is collected in large amounts any given second. The collected data is rich in information, as one can detect accidents (e.g. cars) in real time, or assess injury/sickness over a given time span (e.g. health devices). Due to its chaotic nature and massive amounts of datapoints, timeseries are hard to label manually. Furthermore new classes within the data could emerge over time (contrary to e.g. handwritten digits), which would require relabeling the data.In this paper we present SuSL4TS, a deep generative Gaussian mixture model for semi-unsupervised learning, to classify time series data. With our approach we can alleviate manual labeling steps, since we can detect sparsely labeled classes (semi-supervised) and identify emerging classes hidden in the data (unsupervised). We demonstrate the efficacy of our approach with established time series classification datasets from different domains.

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Section: Methodsmentioning

confidence: 99%