Adaptive Two-Dimensional Embedded Image Clustering

Authors: Zhihui Li, Lina Yao, Sen Wang, Salil Kanhere, Xue Li, Huaxiang Zhang4796-4803

AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Extensive experiments have been conducted on various benchmark datasets. The experimental results confirm the superiority of the proposed algorithm.
Researcher Affiliation Academia 1School of Information Science and Engineering, Shandong Normal University 2School of Computer Science and Engineering, University of New South Wales 3School of Information Technology and Electric Engineering, The University of Queensland
Pseudocode Yes Algorithm 1: Optimization Algorithm for A2DEIC
Open Source Code No The paper does not provide any explicit statement or link for open-source code availability.
Open Datasets Yes UUIm Head Post and Gaze dataset (Weidenbacher et al. 2007):... CVL handwritten dataset (Mouch ere et al. 2013):... Pointing 04 Head Pose dataset (Gourier, Hall, and Crowley 2004):... USPS handwritten digit dataset:... Coil20 object dataset (Nene, Nayar, and Murase 1996):
Dataset Splits No The paper mentions using several datasets and tuning parameters, but does not specify any explicit training, validation, or test dataset splits (e.g., percentages or counts) or methods for creating such splits.
Hardware Specification No The paper does not provide any specific details about the hardware (e.g., GPU or CPU models, memory) used for running the experiments.
Software Dependencies No The paper does not provide specific software dependencies (e.g., library names with version numbers) needed to replicate the experiments.
Experiment Setup Yes For fair comparison, we tune the parameters of all the compared algorithms by grid search, from the range of {10 3, 10 2, 10 1, 100, 101, 102, 103}. ... we fix the parameter λ at 1.