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..
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
Authors: Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The outperforming experimental results show that our method is with high accuracy and much faster than the state-of-the-art methods under several benchmarks with large numbers of high-dimensional data. |
| Researcher Affiliation | Academia | Han Liu , Zhizhong Han , Yu-Shen Liu , Ming Gu School of Software, Tsinghua University, Beijing, China BNRist & KLISS, Beijing, China Department of Computer Science, University of Maryland, College Park, USA |
| Pseudocode | Yes | The outline of the FLRML algorithm is shown in Algorithm 1. It can be mainly divided into four stages: SVD preprocessing (line 2), constant initializing (line 3), variable initializing (lines 4 and 5), and the iterative optimization (lines 6 to 11). |
| Open Source Code | Yes | Code has been made available at https://github.com/highan911/FLRML. |
| Open Datasets | Yes | The methods are evaluated on eight datasets with high dimensions or large numbers of samples: three datasets NG20, RCV1-4 and TDT2-30 derived from three text collections respectively [35, 36]; one handwritten characters dataset MNIST [37]; four voxel datasets of 3D models M10-16, M10-100, M40-16, and M40-100 with different resolutions in 163 and 1003 dimensions, respectively, generated from Model Net10 and Model Net40 [38] which are widely used in 3D shape understanding [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]. |
| Dataset Splits | No | The dimensions D, the number of training samples n, the number of test samples ntest, and the number of categories ncat of all the datasets are listed in Table 1. |
| Hardware Specification | Yes | All the experiments are performed on the Matlab R2015a platform on a PC with 3.60GHz processor and 16GB of physical memory. |
| Software Dependencies | Yes | All the experiments are performed on the Matlab R2015a platform on a PC with 3.60GHz processor and 16GB of physical memory. |
| Experiment Setup | Yes | For all these methods, the rank for SVD is set as r = min(rank(X), 3000). For the other methods, 5 triplets are randomly generated for each sample, which is also used as 5 positive pairs and 5 negative pairs for the methods using pairwise constraints. The accuracy is evaluated by a 5-NN classi๏ฌer using the output metric of each method. For each low-rank metric learning method, the rank constraint for M is set as d = 100. So we use m/ ly = 1 in the experiments in Table 3 and Table 4. So in Table 4, all the results are obtained with Nt = 80 and T = 20. |