Robust Principal Component Analysis with Side Information
Authors: Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon
ICML 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we conduct synthetic experiments as well as a real application on noisy image classification to show that our method also improves the performance in practice by exploiting side information. |
| Researcher Affiliation | Academia | Kai-Yang Chiang? EMAIL Cho-Jui Hsieh EMAIL Inderjit S. Dhillon? EMAIL ?Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA Department of Statistics and Computer Science, University of California at Davis, Davis, CA 95616, USA |
| Pseudocode | Yes | Algorithm 1 ALM method for PCPF |
| Open Source Code | No | The paper mentions using a third-party ALM solver for PCP but does not provide any link or statement about the availability of their own code for PCPF. |
| Open Datasets | Yes | We consider the digit recognition dataset MNIST, which includes 50,000 training images and 10,000 testing images, and each image is a handwriting digit described as a 784 dimensional vector. |
| Dataset Splits | No | The paper mentions training and testing sets for MNIST but does not specify a validation set or explicit split percentages for all three. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper mentions using LIBLINEAR and LIBSVM with citations but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The parameters λ in both PCP and PCPF are set as 1/pn by default as Theorem 1 suggested. The convergence criterion is set to be k R S XHY T k F /k Rk F < 10 7 as suggested in Cand es et al. (2011). |