Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
Authors: Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 classifier 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. |