Low-Rank Multi-View Learning in Matrix Completion for Multi-Label Image Classification
Authors: Meng Liu, Yong Luo, Dacheng Tao, Chao Xu, Yonggang Wen
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimentation on the challenging PASCAL VOC 07 dataset demonstrates the superiority of lr MMC compared to other multi-label image classification approaches. |
| Researcher Affiliation | Academia | Key Laboratory of Machine Perception (MOE), School of EECS, PKU, Beijing 100871, China Center for Quantum Computation and Intelligent Systems, UTS, Sydney, NSW 2007, Australia Division of Networks and Distributed Systems School of Computer Engineering, NTU, 639798, Singapore {lemolemac, yluo180}@gmail.com, dacheng.tao@uts.edu.au, xuchao@cis.pku.edu.cn, ygwen@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1: The learning procedure of low-rank multi-view learning (lr MVL) method. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We use the very challenging PASCAL VOC 07 dataset (Everingham et al. 2007) |
| Dataset Splits | Yes | The standard VOC test set (Everingham et al. 2007) is used for testing, and 20% of the 4,952 test images are randomly selected for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (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 | The parameter μ in lr MVL is determined as in MC-1, and the parameter γ is tuned over the set {10i|i = 4, . . . , 3}. The algorithm stops the iteration when the difference of the objective function is smaller than 10 3. |