Multi-Component Nonnegative Matrix Factorization
Authors: Jing Wang, Feng Tian, Xiao Wang, Hongchuan Yu, Chang Hong Liu, Liang Yang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on real-world datasets have shown that MCNMF not only achieves more accurate performance over the state-of-the-arts using the aggregated representation, but also interprets data from different aspects with the multiple representations, which is beyond what current NMFs can offer. |
| Researcher Affiliation | Academia | Jing Wang1, Feng Tian1, Xiao Wang2, , Hongchuan Yu3, Chang Hong Liu4, Liang Yang5 1Faculty of Science and Technology, Bournemouth University, UK 2Department of Computer Science and Technology, Tsinghua University, China 3National Centre for Computer Animation, Bournemouth University, UK 4Department of Psychology, Bournemouth University, UK 5School of Information Engineering, Tianjin University of Commerce, China |
| Pseudocode | No | The paper provides mathematical derivations for its optimization scheme but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The Yale contains 11 facial images for each of 15 subjects. Sample images are shown in Figure 1. For each subject, its face images are either in different facial expressions (such as happy or sad), or configurations (such as with or without glasses). The ORL2 dataset consists of 400 facial images belonging to 40 different subjects. Similar to the Yale dataset, the images were taken with various lighting and facial expressions. The Notting-Hill [Cao et al., 2015] is a video face dataset, which is derived from the movie Notting Hill . The faces of 5 main casts were used, including 4660 faces in 76 tracks. The COIL20 image library3 is composed of 1440 images for 20 objects. The 72 images of each object were captured by a fixed camera at a pose intervals of 5 degree. For this dataset, we regard the different poses and shapes as components. Footnotes: "1http://www.cad.zju.edu.cn/home/dengcai/Data/Face Data.html", "2http://www.cad.zju.edu.cn/home/dengcai/Data/Face Data.html", "3http://www1.cs.columbia.edu/CAVE/software/softlib/coil20.php" |
| Dataset Splits | No | The paper uses k-means for clustering evaluation and repeats the process multiple times, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any details about the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For MCNMF, we varied the regularization parameter α within [0.01, 0.05] with 0.01 interval and fixed the number of components V = 3 (more discussion in next subsection). In addition, we set each k(i) equals to number of clusters according to the groundtruth of each dataset. The dimensions of obtained optimal representations H for all the compared methods were all set to be k = V i=1 k(i) for fair comparison. |