Uncorrelated Group LASSO
Authors: Deguang Kong, Ji Liu, Bo Liu, Xuan Bao
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on real world datasets demonstrate the effectiveness of the proposed new regularization and algorithm. and To validate the effectiveness of our method, we conduct experiments on multi-label datasets for image annotation. |
| Researcher Affiliation | Collaboration | Deguang Kong1, Ji Liu2, Bo Liu3 and Xuan Bao4 1Samsung Research America, 2University of Rochester, 3Philips Research North America, 4Google Inc. |
| Pseudocode | No | The paper describes the optimization algorithm through text and mathematical equations (Eq. 11, 12) and iterative steps, but does not present it as a clearly labeled pseudocode or algorithm block/figure. |
| Open Source Code | No | The paper does not provide any specific repository links, explicit code release statements, or mention code in supplementary materials for the described methodology. |
| Open Datasets | Yes | Take the House dataset1 (n=506, p=14) as an example. ... 1http://archive.ics.uci.edu/ml/datasets/housing and Barcelona5 dataset contains... 5http://mlg.ucd.ie/content/view/61 and MSRC6 dataset contains... 6http://research.microsoft.com/en-us/projects/\ objectclassrecognition/ and TREVID20057 dataset contains... 7http://www-nlpir.nist.gov/projects/tv2005/ |
| Dataset Splits | Yes | In all the experiments, we use 5-fold cross validation, where 4-fold data are used for training and the remaining ones are used for testing purpose. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions various software concepts and methods, but does not provide specific version numbers for any ancillary software or libraries used in its experiments. |
| Experiment Setup | Yes | In our approach of Eq.(2), we use logistic loss function, group Gg is generated according to the feature correlation defined in Eq.(9), where θ = 0.3. and We adjust the parameter α such that the number of nonzero rows in W (i.e., optimal solution) is r. |