Compressive Sensing of Signals from a GMM with Sparse Precision Matrices
Authors: Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is demonstrated extensively on compressive sensing of imagery and video, and the results with simulated and hardware-acquired real measurements show significant performance improvement over state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Duke University 2Department of Statistics & Department of Computer Science, University of Chicago |
| Pseudocode | No | The paper describes the steps of its variational EM algorithm but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper mentions a website for complete results and other settings, but it does not explicitly state that the source code for the methodology is available there or elsewhere. |
| Open Datasets | Yes | The USPS handwritten digits dataset 3 and the face dataset [28] are used in this experiment. It is downloaded from http://cs.nyu.edu/ roweis/data.html. |
| Dataset Splits | No | The paper describes the datasets used and the number of images/frames, but it does not provide specific training, validation, or test split percentages, counts, or explicit instructions for reproducibility. |
| Hardware Specification | No | The paper mentions 'hardware-acquired real measurements' and 'CACTI camera [6]' which refer to the data acquisition hardware, not the computing hardware (CPUs, GPUs, etc.) used for running the experiments. |
| Software Dependencies | No | The paper mentions various methods/algorithms (e.g., KSVD-OMP, Tw IST, GAP) and a 'single pixel camera' setup, but it does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For the proposed methods, the hyperparameters of the scaled mixture of Gaussians are set as pa0/b0/N ≈ 300, c0 = d0 = 10^-6, the hyperparameter of Dirichlet prior α0 is set as a vector with all elements being one, the hyperparameters of the mean of each Gaussian component are set as β0 = 1, and m0 is set to the mean of the initialization of {bxi}N i=1. We fixed κ = 10^-6 for the proposed methods, GMM-TP and PLE. |