Robust PCA with compressed data
Authors: Wooseok Ha, Rina Foygel Barber
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first use simulated data to study the behavior of the convex program (2) for different compression dimensions, signal complexities and missing levels, which show the close agreement with the scaling predicted by our theory. We also apply our method to a data set consisting of chlorine measurements across a network of sensors. |
| Researcher Affiliation | Academia | Wooseok Ha University of Chicago haywse@uchicago.edu Rina Foygel Barber University of Chicago rina@uchicago.edu |
| Pseudocode | No | The paper describes the convex program (2) and its optimization approach but does not include a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide explicit statements about the release of its source code or links to a code repository. |
| Open Datasets | Yes | Data obtained from http://www.cs.cmu.edu/afs/cs/project/spirit-1/www/ |
| Dataset Splits | Yes | In order to evaluate performance, we use 80% of the entries to fit the model, 10% as a validation set for selecting tuning parameters, and the final 10% as a test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For simplicity, in all experiments, we select = 1, which is easier for optimization and generally results in a solution that still has low spikiness (that is, the solution is the same as if we had imposed a bound with finite ). All results are averaged over 5 trials. |