Learning Mixtures of Linear Dynamical Systems
Authors: Yanxi Chen, H. Vincent Poor
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical studies with numerical experiments, confirming the efficacy of the proposed algorithm. [...] In these experiments, we fix d = 80, K = 4; moreover, let Tsubspace = 20, Tclustering = 20 and Tclassification = 5, all of which are much smaller than d. We take |Msubspace| = 30 d, |Mclustering| = 10 d, and vary |Mclassification| between [0, 5000 d]. Our experiments focus on Case 1 as defined in (8b), and we generate the labels of the sample trajectories uniformly at random. |
| Researcher Affiliation | Academia | Yanxi Chen 1 H. Vincent Poor 1 1Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA. Correspondence to: Yanxi Chen <yanxic@princeton.edu>. |
| Pseudocode | Yes | Algorithm 1 A two-stage algorithm for mixed LDSs [...] Algorithm 2 Subspace estimation [...] Algorithm 3 Clustering [...] Algorithm 4 Least squares and covariance estimation [...] Algorithm 5 Classification |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide links to a code repository. |
| Open Datasets | Yes | In our experiments, we work with the Motion Sense dataset (Malekzadeh et al., 2019). |
| Dataset Splits | No | The paper discusses various parameters and subsets of data used (Msubspace, Mclustering, Mclassification, Tsubspace, Tclustering, Tclassification), but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) in a way that would allow for reproduction of data partitioning for model evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud computing instances) used to run the experiments. |
| Software Dependencies | No | The paper describes algorithms and theoretical aspects but does not list specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') that would be needed for replication. |
| Experiment Setup | Yes | In these experiments, we fix d = 80, K = 4; moreover, let Tsubspace = 20, Tclustering = 20 and Tclassification = 5, all of which are much smaller than d. We take |Msubspace| = 30 d, |Mclustering| = 10 d, and vary |Mclassification| between [0, 5000 d]. |