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Keywords	Visualization, Dimensionality Reduction, Manifold learning +
Level	Master +
OneLineSummary	Study and development of tools and methods for the visualization of (temporal) human activity patterns. +
Prerequisites	Completed courses in basic machine learning is required. +
References	Maaten, L. V. D., & Hinton, G. (2008). … Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579-2605. Lundström, J., Järpe, E., & Verikas, A. (2016). Detecting and exploring deviating behaviour of smart home residents. Expert Systems with Applications, 55, 429-440. Rauber, P. E., Falcão, A. X., & Telea, A. C. (2016). Visualizing time-dependent data using dynamic t-SNE. Proc. EuroVis Short Papers, 2(5). Cheng, J., Liu, H., Wang, F., Li, H., & Zhu, C. (2015). Silhouette analysis for human action recognition based on supervised temporal t-sne and incremental learning. IEEE Transactions on Image Processing, 24(10), 3203-3217.ns on Image Processing, 24(10), 3203-3217.
StudentProjectStatus	Open +
Supervisors	Jens Lundström + , Eric Järpe + , Rebeen Hamad +
Title	Comprehending low-dimensional manifolds of temporal data from the home +
Categories	StudentProject +
Modification dateThis property is a special property in this wiki.	5 October 2017 09:17:04 +

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