{"type":"ScholarlyArticle","title":"Robust Iterative Quantization for Efficient ℓp-norm Similarity Search","authors":[{"name":"Yuchen Guo","slug":"yuchen-guo","given":"Yuchen","family":"Guo","affiliation":""},{"name":"Guiguang Ding","slug":"guiguang-ding","given":"Guiguang","family":"Ding","affiliation":""},{"name":"Jungong Han","slug":"jungong-han","given":"Jungong","family":"Han","affiliation":""},{"name":"Xiaoming Jin","slug":"xiaoming-jin","given":"Xiaoming","family":"Jin","affiliation":""}],"abstract":"Iterative Quantization (ITQ) is one of the most successful hashing based nearest-neighbor search methods for large-scale information retrieval in the past a few years due to its simplicity and superior performance. However, the performance of this algorithm degrades significantly when dealing with noisy data. Additionally, it can barely facilitate a wide range of applications as the distortion measurement only limits to ℓ2 norm. In this paper, we propose an ITQ+ algorithm, aiming to enhance both robustness and generalization of the original ITQ algorithm. Specifically, a ℓp,q-norm loss function is proposed to conduct the ℓp-norm similarity search, rather than a ℓ2} norm search. Despite the fact that changing the loss function to ℓp,q-norm makes our algorithm more robust and generic, it brings us a challenge that minimizes the obtained orthogonality constrained ℓp,q-norm function, which is non-smooth and non-convex. To solve this problem, we propose a novel and efficient optimization scheme. Extensive experiments on benchmark datasets demonstrate that ITQ+ is overwhelmingly better than the original ITQ algorithm, especially when searching similarity in noisy data.","doi":null,"url":"https://ijcai.org/proceedings/2016/478","pdf":"https://ijcai.org/proceedings/2016/0478.pdf","markdown":"https://ijcai.org/proceedings/2016/478.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/478","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":478,"pages":{"first":3382,"last":3387},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}