Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Non-Convex Feature Learning via Lp,inf Operator
Authors: Deguang Kong, Chris Ding
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive experiments are conducted to characterize the effect of varying p and to compare with other approaches on real world multi-class and multilabel datasets. |
| Researcher Affiliation | Academia | Deguang Kong and Chris Ding Department of Computer Science & Engineering, University of Texas, Arlington, 500 UTA Blvd, Arlington, TX 76010 EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1 Proximal operator solution for Eq.(7) when 0 < p 1 |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | For multi-class datasets, we use four widely used biology datasets: ALLAML1, GLIOMAML2, LUNGML3 and CARML4, which all have high-dimensional features (more than 3000) and very few samples (less than 250). We use three widely used multilabel datasets, Barcelona5, MSRCv26, TRECVID20057. ... 1http://www.sciencemag.org/content/277/5324/393.full 2http://cancerres.aacrjournals.org/content/63/7/1602.long ... 7http://www-nlpir.nist.gov/projects/tv2005/ |
| Dataset Splits | Yes | We did 5-fold cross-validation on both classifiers on 3 datasets: ALLAML, GLIOMAML and LUNGML. ... We report the macro F1 score by using 5 round 5-fold cross validation in Fig.3. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using classifiers like SVM and kNN, and general software (e.g., 'standard least square loss'), but it does not list any specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We use standard SVM classifier (linear kernel, C = 1) to validate the feature selection results. |