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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Low-Rank Similarity Metric Learning in High Dimensions
Authors: Wei Liu, Cun Mu, Rongrong Ji, Shiqian Ma, John Smith, Shih-Fu Chang
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficacy of the proposed algorithm is demonstrated through experiments performed on four benchmark datasets with tens of thousands of dimensions. |
| Researcher Affiliation | Collaboration | IBM T. J. Watson Research Center Columbia University Xiamen University The Chinese University of Hong Kong EMAIL EMAIL EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Low-Rank Similarity Metric Learning |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We carry out the experiments on four benchmark datasets including two document datasets Reuters-28 and TDT2-30 (Cai, He, and Han 2011), and two image datasets UIUCSports (Li and Fei-Fei 2007) and UIUC-Scene (Lazebnik, Schmid, and Ponce 2006). |
| Dataset Splits | Yes | On Reuters-28 and TDT2-30, we select 5 C up to 30 C samples for training such that each category covers at least one sample; we pick up the same number of samples for cross-validation; the rest of samples are for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | To run our proposed method LRSML, we fix ϵ = 0.1, ρ = 1, and find that τ = 0.01 makes the linearized ADMM converge within T = 1, 000 iterations on all datasets. |