Enhanced Unsupervised Discriminant Dimensionality Reduction for Nonlinear Data
Authors: Qianqian Wang, Mengping Jiang, Wei Feng, Zhengming Ding
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on seven datasets demonstrate the effectiveness of the proposed method. Our experiments were conducted on a Windows 11 desktop computer with a 13th Gen Intel(R) Core(TM) CPU, and MATLAB R2023a. |
| Researcher Affiliation | Academia | Qianqian Wang1 , Mengping Jiang1 , Wei Feng2 and Zhengming Ding3 1School of Communication Engineering, Xidian University, Xi an, China 2College of Information Engineering, Northwest A&F University, Yangling, China 3Department of Computer Science, Tulane University, New Orleans, LA EMAIL, EMAIL, EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 The whole process of solving problem (17) Input: A set of input data X; hyperparameter λ Initialization: W: setting an identity matrix to the first t rows; F: setting an identity matrix to every m rows Output: Projection matrix W Rd t, cluster indication matrix F Rn m 1: while not converge do 2: Update F by solving Eq. (25); 3: Update W by solving Eq. (20); 4: end while 5: return Projection matrix W, cluster indicator matrix F |
| Open Source Code | No | The paper does not contain any explicit statement about releasing code, nor does it provide a link to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | We selected seven benchmark datasets to verify the performance of our proposed method, which are Face V5 [Team, 2009], Isolet [Fanty and Cole, 1990], JAFFE [Lyons et al., 1999], MSRC V2 [Winn and Jojic, 2005], ORL [Cai et al., 2010], UMIST [Hou et al., 2013] and Yaleface [Georghiades et al., 1997]. The details of these benchmark datasets are shown in Table 1. |
| Dataset Splits | No | The paper uses seven benchmark datasets for clustering, but does not specify training/test/validation splits. It mentions, 'to ensure accuracy, we repeat the experiment ten times and then take the maximum value,' which relates to experimental runs, not data partitioning into splits. |
| Hardware Specification | Yes | Our experiments were conducted on a Windows 11 desktop computer with a 13th Gen Intel(R) Core(TM) CPU, and MATLAB R2023a. |
| Software Dependencies | Yes | Our experiments were conducted on a Windows 11 desktop computer with a 13th Gen Intel(R) Core(TM) CPU, and MATLAB R2023a. |
| Experiment Setup | Yes | Our proposed method can achieve the best clustering result for the ORL dataset when λ is 0.08, and the remaining six datasets Face V5, Isolet, JAFFE, MSRC V2, UMIST and Yaleface have the best clustering performance when λ is equal to or close to 0.05. Moreover, the different dimensions of the subspace will also affect the clustering results, so We sequentially select the subspace dimensions ranging from 150 to the dimension d of data X with an interval of 20 for traversal. |