{"type":"ScholarlyArticle","title":"Incomplete Multi-Modal Visual Data Grouping","authors":[{"name":"Handong Zhao","slug":"handong-zhao","given":"Handong","family":"Zhao","affiliation":""},{"name":"Hongfu Liu","slug":"hongfu-liu","given":"Hongfu","family":"Liu","affiliation":""},{"name":"Yun Fu","slug":"yun-fu","given":"Yun","family":"Fu","affiliation":""}],"abstract":"Nowadays multi-modal visual data are much easier to access as the technology develops. Nevertheless, there is an underlying problem hidden behind the emerging multi-modality techniques: What if one/more modal data fail? Motivated by this question, we propose an unsupervised method which well handles the incomplete multi-modal data by transforming the original and incomplete data to a new and complete representation in a latent space. Different from the existing efforts that simply project data from each modality into a common subspace, a novel graph Laplacian term with a good probabilistic interpretation is proposed to couple the incomplete multi-modal samples. In such a way, a compact global structure over the entire heterogeneous data is well preserved, leading to a strong grouping discriminability. As a non-trivial contribution, we provide the optimization solution to the proposed model. In experiments, we extensively test our method and competitors on one synthetic data, two RGB-D video datasets and two image datasets. The superior results validate the benefits of the proposed method, especially when multi-modal data suffer from large incompleteness.","doi":null,"url":"https://ijcai.org/proceedings/2016/341","pdf":"https://ijcai.org/proceedings/2016/0341.pdf","markdown":"https://ijcai.org/proceedings/2016/341.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/341","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":341,"pages":{"first":2392,"last":2397},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}