{"type":"ScholarlyArticle","title":"A Robust Noise Resistant Algorithm for POI Identification from Flickr Data","authors":[{"name":"Yiyang Yang","slug":"yiyang-yang","given":"Yiyang","family":"Yang","affiliation":"Department of Computer and Information Science , University of Macau , Macau"},{"name":"Zhiguo Gong","slug":"zhiguo-gong","given":"Zhiguo","family":"Gong","affiliation":"§ Faculty of Computer , Guangdong University of Technology , Guangzhou , China"},{"name":"Qing Li","slug":"qing-li","given":"Qing","family":"Li","affiliation":"¶ SAR"},{"name":"Leong Hou U","slug":"leong-hou-u","given":"Leong Hou","family":"U","affiliation":"§ Faculty of Computer , Guangdong University of Technology , Guangzhou , China; School of Mathematics and Big Data , Foshan University , Foshan , China"},{"name":"Ruichu Cai","slug":"ruichu-cai","given":"Ruichu","family":"Cai","affiliation":"Department of Computer and Information Science , University of Macau , Macau"},{"name":"Zhifeng Hao","slug":"zhifeng-hao","given":"Zhifeng","family":"Hao","affiliation":"‡ Department of Computer Science , City University of Hong Kong , Hong Kong SAR"}],"abstract":"Point of Interests (POI) identification using social media data (e.g. Flickr, Microblog) is one of the most popular research topics in recent years. However, there exist large amounts of noises (POI irrelevant data) in such crowd-contributed collections. Traditional solutions to this problem is to set a global density threshold and remove the data point as noise if its density is lower than the threshold. However, the density values vary significantly among POIs. As the result, some POIs with relatively lower density could not be identified. To solve the problem, we propose a technique based on the local drastic changes of the data density. First we define the local maxima of the density function as the Urban POIs, and the gradient ascent algorithm is exploited to assign data points into different clusters. To remove noises, we incorporate the Laplacian Zero-Crossing points along the gradient ascent process as the boundaries of the POI. Points located outside the POI region are regarded as noises. Then the technique is extended into the geographical and textual joint space so that it can make use of the heterogeneous features of social media. The experimental results show the significance of the proposed approach in removing noises.","doi":"10.24963/ijcai.2017/460","url":"https://ijcai.org/proceedings/2017/460","pdf":"https://ijcai.org/proceedings/2017/0460.pdf","markdown":"https://ijcai.org/proceedings/2017/460.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/460","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":460,"pages":{"first":3294,"last":3300},"keywords":["Machine Learning: Unsupervised Learning","Multidisciplinary Topics and Applications: AI and Social Sciences","Machine Learning: Data Mining","Machine Learning: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Bui et al., 2015] Thanh-Hieu Bui, Yong-Jin Han, Seong- Bae Park, and Se-Young Park. 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