Multi-Frequency Vector Diffusion Maps
Authors: Yifeng Fan, Zhizhen Zhao
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the efficacy of MFVDM on synthetic data generated according to a random graph model and cryo-electron microscopy image dataset. The new method achieves better nearest neighbor search and alignment estimation than the state-of-the-arts VDM and diffusion maps (DM) on extremely noisy data. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engeneering, Coordinated Science Laboratory, University of Illinois at Urbana Champaign, Illinois, USA. |
| Pseudocode | Yes | Algorithm 1 Joint nearest neighbor search and alignment |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses synthetic data generated by the authors ("We simulate n = 104 points xi uniformly distributed over SO(3)" and "we simulate n = 104 projection images from a 3-D electron density map of the 70S ribosome"). It does not provide access information or citations for these simulated datasets to indicate public availability. |
| Dataset Splits | No | The paper discusses synthetic and simulated data generation and evaluation of nearest neighbors, but it does not specify training, validation, and test splits with percentages or sample counts in the traditional sense for dataset partitioning. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., CPU, GPU models, or cloud computing instances). |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | For MFVDM, we set the maximum frequency kmax = 50 and for each k, we select top mk = 50 eigenvectors. For VDM and DM, we set the number of eigenvectors to be m = 50. In addition, we set random walk t = 1. ... We set t = 10, kmax = 10, mk = 10, and m = 10 for MFVDM, VDM, and DM respectively. |