Context Aware Tensor Decomposition method for our KDD 2014 and UbiComp 2014 paper listed below.
[1] Yilun Wang*, Yu Zheng, Yexiang Xue. Travel Time Estimation of a Path using Sparse Trajectories. In the Proc. of KDD 2014
[2] Yu Zheng, Tong Liu, Yilun Wang, Yanchi Liu, Yanmin Zhu, Eric Chang. Diagnosing New York City’s Noises with Ubiquitous Data. In Proceedings of UbiComp 2014.
[3] Yilun Wang, Yu Zheng, Tong Liu. A noise map of New York City. In Proc. of UbiComp 2014.
- File folder TensorData encloses the text files of features extracted from original datasets.
- TransformTxt2Mat.m reads the text files and transforms them by normalization. The results are saved as .mat files enclosed in TensorMat file folder.
- TensorForExperiment.m runs tensor decomposition (TD) with four different settings: (1) TD without features, (2) TD plus spatial feature extracted POIs and road networks (TD+B), (3) TD+B plus check-in feature (TD+B+D), and (4) TD+B+D plus the feature of noise category correlations (TD+B+C+D). To evaluate the performance, we randomly choose 70% data as train data and other 30% data as test data. The error metrics of the results of TD are RMSE and MAE.
- TensorForDemo.m obtains the result of tensor decomposition with all three features without choosing 70% data as train data. The result is stored in .mat files, in folder TensorResult.