Tensorized Label Learning on Anchor Graph
Authors: Jing Li, Quanxue Gao, Qianqian Wang, Wei Xia
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we estimate the performance of our algorithm on 5 extensively used multi-view datasets and compare our algorithm with 7 state-of-art multi-view clustering methods. |
| Researcher Affiliation | Academia | 1School of Telecommunications Engineering, Xidian University jinglxd@stu.xidian.edu.cn, qxgao@xidian.edu.cn, qqwang@xidian.edu.cn, xdweixia@gmail.com |
| Pseudocode | Yes | Algorithm 1: Tensorized Label Learning on Anchor Graph (TLL-AG) |
| Open Source Code | Yes | Detailed experimental configurations and results are in the appendix. It s available at https://github.com/xdjingli/TLL-AG. |
| Open Datasets | Yes | MSRC (Winn and Jojic 2005), Hand Written4 (Dua and Graff 2017), Mnist4 (Le Cun et al. 1998), Reuters(Apt e, Damerau, and Weiss 1994), Noisy MNIST (Wang et al. 2015);. The details of the above datasets are shown in Table 1. |
| Dataset Splits | No | The paper uses various multi-view datasets (e.g., MSRC, Hand Written4, Mnist4, Reuters, Noisy MNIST) but does not provide specific details on how these datasets were split into training, validation, and testing sets, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific library versions, programming language versions, or solver versions) needed to replicate the experiments. |
| Experiment Setup | Yes | In this subsection, we will analyze the effect of variable parameters on Hand Written4 and Mnist4. Effect of Anchor Rate Our model uses anchor graph to perform semi-nonnegative matrix factorization. (...) Effect of Parameter p We set the value of p to vary from 0.1 to 1 in intervals of 0.1. (...) Effect of Parameter β We set the value of β from {10, 500, 2000, 5000, 10000} for Hand Written4 and Mnist4... |