@inproceedings{
li2024dual,
title={Dual Advancement of Representation Learning and Clustering for Sparse and Noisy Images},
author={Wenlin Li and Yucheng Xu and Xiaoqing Zheng and Suoya Han and Jun Wang and Xiaobo Sun},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=j2M0047AQA}
}
Sparse and noisy images (SNIs), like those  in spatial gene expression data, pose significant challenges for effective representation learning and clustering, which are essential for thorough data analysis and interpretation. In response to these challenges, we propose $\textbf{D}$ual $\textbf{A}$dvancement of $\textbf{R}$epresentation $\textbf{L}$earning and $\textbf{C}$lustering ($\textit{\textbf{DARLC}}$), an innovative framework that leverages contrastive learning to enhance the representations derived from masked image modeling. Simultaneously, $\textit{DARLC}$ integrates cluster assignments in a cohesive, end-to-end approach. This integrated clustering strategy addresses the ``class collision problem'' inherent in contrastive learning, thus improving the quality of the resulting representations. To generate more plausible positive views for contrastive learning, we employ a graph attention network-based technique that produces denoised images as augmented data. As such, our framework offers a comprehensive approach that improves the learning of representations by enhancing their local perceptibility, distinctiveness, and the understanding of relational semantics. Furthermore, we utilize a Student's t mixture model to achieve more robust and adaptable clustering of SNIs. Extensive evaluation on 12 real-world datasets of SNIs, representing spatial gene expressions, demonstrat $\textit{DARLC}$'s superiority over current state-of-the-art methods in both image clustering and generating representations that accurately reflect biosemantics content and gene interactions.
Dual Advancement of Representation Learning and Clustering for Sparse and Noisy Images
Wenlin Li Yucheng Xu Xiaoqing Zheng Suoya Han Jun Wang Xiaobo Sun