ICDM 2022 Workshop on Multi-view Representation Learning

With the advent of big data era, multi-view data become ubiquitous in real world. How to mine the valuable information inside these multi-view data is of significant importance. As a key component of various intelligent data analyticor mining algorithms, multi-view representation learning attracted considerable attentions, especially along with the resurgence of deep learning.
Although multi-view representation learning has gained great progress, there are still some open problems that requires further investigation. For instance, most existing multi-view representation learning methods can not deal with missing values and unaligned views directly. Big data era calls for efficient multi-view representation learning methods. Deep multi-view representation learning methods have huge parameters to tune and lack interpretability. Compared with other machine learning tasks like classification, multi-view representation learning does not have a clear criterion or good evaluation. All these problems call for novel multi-view representation learning theory and algorithms.
Multi-view representation learning has been successfully applied to many applications including computer vision, natural language processing, speech recognition, signal processing. Extending its applications to more areas needs more novel advanced multi-view representation learning theory, algorithms and high-quality datasets.
This workshop aims to provide a forum for researchers and practitioners from different domains to share their new findings and communicate to advance the further development of multi-view representation learning.

Submission information: We invite both full papers (max 8 pages) describing mature work and short papers (max 5-6 pages) describing work-in-progress or case studies. Only original and high-quality papers formatted using the IEEE2-column format (Latex Template), including the bibliography and any possible appendices will be considered for reviewing. Paper submission site is at https://wi-lab.com/cyberchair/2022/icdm22/scripts/ws_submit.php?subarea=S.
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