Top Rank Optimization in Linear Time
Authors: Nan Li, Rong Jin, Zhi-Hua Zhou
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical study shows that the proposed approach is highly competitive to the state-of-the-art approaches and is 10-100 times faster. To evaluate the performance of the Top Push algorithm, we conduct a set of experiments on realworld datasets. Table 2 (left column) summarizes the datasets used in our experiments. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824 {lin,zhouzh}@lamda.nju.edu.cn rongjin@cse.msu.edu |
| Pseudocode | Yes | Algorithm 1 The Top Push Algorithm |
| Open Source Code | No | The paper does not provide any links to its source code or explicitly state that its code is publicly available. |
| Open Datasets | Yes | Table 2 (left column) summarizes the datasets used in our experiments. Some of them were used in previous studies [1, 31, 3], and others are larger datasets from different domains. |
| Dataset Splits | Yes | In each trial, the dataset is randomly divided into two subsets: 2/3 for training and 1/3 for test. For all algorithms, we set the precision parameter ϵ to 10 4, choose other parameters by 5-fold cross validation (based on the average value of Pos@Top) on training set, and perform the evaluation on test set. |
| Hardware Specification | Yes | All experiments are run on a machine with two Intel Xeon E7 CPUs and 16GB memory. |
| Software Dependencies | Yes | We implement Top Push and Infinite Push using MATLAB, implement AATP using CVX [14], and use LIBLINEAR [11] for LR and cs-SVM... [14] refers to 'CVX: Matlab software for disciplined convex programming, version 2.1'. |
| Experiment Setup | Yes | On each dataset, experiments are run for thirty trials. In each trial, the dataset is randomly divided into two subsets: 2/3 for training and 1/3 for test. For all algorithms, we set the precision parameter ϵ to 10 4, choose other parameters by 5-fold cross validation (based on the average value of Pos@Top) on training set, and perform the evaluation on test set. |