Transductive Learning with Multi-class Volume Approximation
Authors: Gang Niu, Bo Dai, Christoffel Plessis, Masashi Sugiyama
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show it compares favorably with the one-vs-rest extension.In this section, we numerically evaluate MAVR. |
| Researcher Affiliation | Collaboration | Gang Niu NIUGANG@BAIDU.COM Tokyo Institute of Technology, Tokyo, 152-8552, Japan Baidu Inc., Beijing, 100085, China Bo Dai BODAI@GATECH.EDU Georgia Institute of Technology, Atlanta, GA 30332, USA Marthinus Christoffel du Plessis CHRISTO@SG.CS.TITECH.AC.JP Masashi Sugiyama SUGI@CS.TITECH.AC.JP Tokyo Institute of Technology, Tokyo, 152-8552, Japan |
| Pseudocode | Yes | Algorithm 1 MAVR Input: P, Q, Y , γ and τ Output: H and ρ 1: Eigen-decompose P and Q; 2: Construct the function g(ρ); 3: Find the smallest root of g(ρ); 4: Recover h using ρ and reshape h to H. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | The latter 3 data sets come from Zelnik-Manor & Perona (2004). |
| Dataset Splits | No | The paper describes a transductive learning setting where prediction is made on unlabeled data (Xu), but it does not specify explicit dataset splits (e.g., percentages or counts) for training, validation, and testing needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU or CPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or framework versions) used for the experiments. |
| Experiment Setup | Yes | For the hyperparameters, we set γ = 99 and τ = l.The default values of factors were σϵ = 0.5, σ = 0.5, l = 3, n = 300, γ = 99, and τ = l, and the ranges of these factors were σϵ 0.5 exp{−1.5, −1.4, −1.3 . . . , 0.5}; l {3, 4, 5, . . . , 20}; n {120, 138, 156, . . . , 480}; σ 0.5 exp{−1, 0.9, 0.8, . . . , 1}; γ 99 exp{−4, −3, −2, . . . , 16}; l exp{−2, −1, 0, . . . , 18}. |