Ranking Preserving Nonnegative Matrix Factorization
Authors: Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results with several datasets for clustering and classification have demonstrated that RPNMF achieves greater performance against the state-of-the-arts, not only in terms of accuracy, but also interpretation of orderly data structure. |
| Researcher Affiliation | Academia | 1 Graduate School of Information Science and Technology, The University of Tokyo, Japan 2 Faculty of Science and Technology, Bournemouth University, UK 3 School of Computer Science and Engineering, The University of New South Wales, Australia 4 School of Computer Science, Beijing University of Posts and Telecommunications, China |
| Pseudocode | No | The paper presents updating rules as mathematical equations (11), (12), (15), (16), (17) but does not provide a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code for the described methodology. |
| Open Datasets | Yes | The Yale [Liu et al., 2012] contains 11 face images for each of 15 subjects. The ORL [Liu et al., 2012] consists of 400 face images of 40 different subjects. The Coil20 [Wang et al., 2017b]is composed of 1440 images for 20 objects. The NHill [Wang et al., 2017b] is a face dataset sampled from the movie Notting Hill. The Cartoon [Wang et al., 2017a] is a video sequence extracted from a short animation available online, which has 282 frames of three scenes. The Hdm05 is a motion capture dataset. As in [Wang et al., 2017a], we chose the scene 1-1 which contains 9842 frames and 14 activities. |
| Dataset Splits | Yes | Since k-means is sensitive to initial values, we repeated the clustering 50 times, each with a new set of initial centroid. Moreover, since all the compared methods converge to local minimum, we ran each method 10 times to avoid randomness. For each dataset, 80% data from each class was randomly selected as training dataset and the rest as testing dataset. |
| Hardware Specification | Yes | All the experiments were done using Matlab 2014 in an Intel Core 3.50GHZ desktop. |
| Software Dependencies | Yes | All the experiments were done using Matlab 2014 in an Intel Core 3.50GHZ desktop. Similar to [Liu and Tsang, 2017; Liu et al., 2017a], the LIBLINEAR package [Fan et al., 2008] was used to train the classifiers. |
| Experiment Setup | Yes | For RPNMF, we varied the regularization parameter α and δ within {0.0001, 0.001, 0.01, 0.1, 1} and {0.001, 0.01, 0.1, 1, 10, 100}, respectively. To construct ordinal relations for t-STE and RPNMF, we first randomly selected 10% data for each dataset, and then constructed 30 ordinal relations for each selected data as in [Chang et al., 2014]. Since k-means is sensitive to initial values, we repeated the clustering 50 times, each with a new set of initial centroid. Moreover, since all the compared methods converge to local minimum, we ran each method 10 times to avoid randomness. |