Browse wiki

Comprehending low-dimensional manifolds of temporal data from the home
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  +
hide properties that link here 
  No properties link to this page.


Enter the name of the page to start browsing from.