High-dimensional Similarity Learning via Dual-sparse Random Projection
Authors: Dezhong Yao, Peilin Zhao, Tuan-Anh Nguyen Pham, Gao Cong
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first present our study on Du RPRSL for ranking and classification tasks. Then we give a case study on the support of dual-sparse regularized Du RPRSL. |
| Researcher Affiliation | Collaboration | Dezhong Yao1, Peilin Zhao2, Tuan-Anh Nguyen Pham1 and Gao Cong3 1 Rolls-Royce@NTU Corporate Lab, Nanyang Technological University, Singapore 2 South China University of Technology; Tencent AI Lab, China 3 School of Computer Science and Engineering, Nanyang Technological University, Singapore |
| Pseudocode | Yes | Algorithm 1 Dual Random Projection Method for Relative Similarity Learning (Du RPRSL) and Algorithm 2 SDCA: Stochastic Dual Coordinate Ascent for Relative Similarity Learning |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | To examine the effectiveness of the proposed method, we tested on six public datasets: Protein, Gisette, RCV1, URL, Caltech256, and BBC from LIBSVM, Caltech, and UCD, as shown in Table 1. |
| Dataset Splits | Yes | For evaluation, we used the standard training and testing split given by the providers, except for Caltch30 and BBC. For these two datasets, we randomly split them into a training set (70%) and a test set (30%). To generate a triplet (xt, x+ t , x t ), xt is firstly randomly selected from the whole training set, then x+ t is randomly selected from the subset of training set, which consists of the examples with the same class of xt, at last, x t is randomly selected from the rest of training set, which consists of the examples with different classes of xt. To make a fair comparison, all methods adopted the same experimental setup. We randomly selected T=50, 000 triplets as training instances and set the number of epochs to be 5 for all stochastic methods. The average results over five trials were reported finally. Cross-validation was used to select the values of hyperparameters for all algorithms. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., 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 make a fair comparison, all methods adopted the same experimental setup. We randomly selected T=50, 000 triplets as training instances and set the number of epochs to be 5 for all stochastic methods. The average results over five trials were reported finally. Cross-validation was used to select the values of hyperparameters for all algorithms. Specifically, the parameters set by cross-validation included: the aggressiveness parameter C for OASIS (C {1, 0.1, 0.08, ..., 0.01}) and λ {5e-2, 5e-1, ..., 5e+6}. Moreover, the hinge loss was used in the implementation of the proposed algorithms. |