Similarity Preserving Representation Learning for Time Series Clustering
Authors: Qi Lei, Jinfeng Yi, Roman Vaculin, Lingfei Wu, Inderjit S. Dhillon
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By conducting extensive empirical studies, we show that the proposed framework is more effective, efficient, and flexible, compared to other state-of-the-art time series clustering methods. We conduct extensive experiments on all the 85 datasets in the UCR time series classification and clustering repository [Chen et al., 2015] |
| Researcher Affiliation | Collaboration | Qi Lei1 , Jinfeng Yi2 , Roman Vaculin3 , Lingfei Wu3 and Inderjit S. Dhillon4,1 1University of Texas at Austin 2JD AI Research 3IBM Research 4Amazon |
| Pseudocode | Yes | Algorithm 1 Efficient Exact Cyclic Coordinate Descent Algorithm for Solving the Optimization Problem (4) |
| Open Source Code | Yes | Our source code and the detailed experimental results are publicly available.2 2https://github.com/cecilialeiqi/SPIRAL |
| Open Datasets | Yes | We conduct extensive experiments on all the 85 datasets in the UCR time series classification and clustering repository [Chen et al., 2015]. The UCR time series classification archive, July 2015. www.cs.ucr.edu/ eamonn/time series data/. |
| Dataset Splits | No | Since data clustering is an unsupervised learning problem, we merge the training and testing sets of all the datasets. |
| Hardware Specification | Yes | All the results were averaged from 5 trials and obtained on a Linux server with an Intel Xeon 2.40 GHz CPU and 256 GB of main memory. |
| Software Dependencies | No | The paper does not specify particular software dependencies with version numbers (e.g., Python version, library versions) used for the implementation or experiments. |
| Experiment Setup | Yes | In our experiments, we set |Ω| = [20n log n], and # features d = 15. The convergence criteria is defined as the objective decreases to be less than 1e-5 in one iteration. To conduct fair comparisons, in all DTW related algorithms and all datasets, we set the DTW window size to be the best warping size reported in [Chen et al., 2015]. |