Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Ranking Preserving Nonnegative Matrix Factorization
Authors: Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi
IJCAI 2018 | Venue PDF | 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. |