Attributed Subspace Clustering

Authors: Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments on several benchmark image datasets have demonstrated the effectiveness of ASC not only in terms of clustering accuracy achieved by the integrated representation, but also the diverse interpretation of data, which is beyond what current approaches can offer.
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 College of Intelligence and Computing, Tianjin University, Tianjin, China
Pseudocode Yes Algorithm 1 Solving ASC
Open Source Code No The paper does not provide any statement or link indicating that the source code for their methodology is openly available.
Open Datasets Yes The Yale1 contains 11 face images for each of 15 subjects. The COIL202 is composed of 1440 images for 20 objects. ...3http://www.cad.zju.edu.cn/home/dengcai/Data/Face Data.html
Dataset Splits No The paper lists datasets used but does not explicitly provide details about training/validation/test splits (e.g., percentages, counts, or a citation to a standard split setup).
Hardware Specification No The paper does not provide any specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments.
Software Dependencies No The paper mentions using "Normalized Cuts [Shi and Malik, 2000]" but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, or specific libraries).
Experiment Setup Yes For ASC, we empirically fixed (λ1, λ2) = (0.2λ3, 0.1λ3) and tuned λ3 from [0.1, 0.2, 0.3, 0.4, 0.5]. We also fixed the number of attributes V = 3 and each reduced dimension k(v) = 50 for all experiments.