Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Authors: Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are provided to support our analysis. We demonstrate the effectiveness of our proposed algorithm using both simulated and real datasets. |
| Researcher Affiliation | Academia | Minshuo Chen1 Lin F. Yang2 Mengdi Wang2 Tuo Zhao1 1Georgia Institute of Technology 2Princeton University 1{mchen393, tourzhao}@gatech.edu 2{lin.yang, mengdiw}@princeton.edu |
| Pseudocode | Yes | Algorithm 1 Downsampled Oja s Algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We adopt the Air Quality dataset (De Vito et al., 2008) |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., library names, framework versions, or programming language versions with libraries). |
| Experiment Setup | Yes | The step size is = 3 10 5, and the algorithm runs with 8 105 total samples. Specifically, we set the step size = 0 h 4000 if k < 2 104, = 0 h 8000 if k 2 [2 104, 5 104), = 0 h 48000 if k 2 [5 104, 10 104), and = 0 h 120000 if k 10 104. We choose 0 in {0.125, 0.25, 0.5, 1, 2} and report the final principle angles achieved by different block sizes h in Table 1. |