Full metadata
Title
Deep Temporal Clustering: Fully Unsupervised Learning of Time-Domain Features
Description
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. This thesis presents a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, a visualization method is applied that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.
Date Created
2018
Contributors
- Madiraju, NaveenSai (Author)
- Liang, Jianming (Thesis advisor)
- Wang, Yalin (Thesis advisor)
- He, Jingrui (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
32 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.50523
Level of coding
minimal
Note
Masters Thesis Computer Engineering 2018
System Created
- 2018-10-01 08:02:46
System Modified
- 2021-08-26 09:47:01
- 3 years 2 months ago
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