{"type":"ScholarlyArticle","title":"Web Page Classification Based on Uncorrelated Semi-Supervised Intra-View and Inter-View Manifold Discriminant Feature Extraction","authors":[{"name":"Xiao-Yuan Jing","slug":"xiao-yuan-jing","given":"Xiao-Yuan","family":"Jing","affiliation":""},{"name":"Qian Liu","slug":"qian-liu-2","given":"Qian","family":"Liu","affiliation":""},{"name":"Fei Wu","slug":"fei-wu-2","given":"Fei","family":"Wu","affiliation":""},{"name":"Baowen Xu","slug":"baowen-xu","given":"Baowen","family":"Xu","affiliation":""},{"name":"Yang-Ping Zhu","slug":"yang-ping-zhu","given":"Yang-Ping","family":"Zhu","affiliation":""},{"name":"Songcan Chen","slug":"songcan-chen","given":"Songcan","family":"Chen","affiliation":""}],"abstract":"Web page classification has attracted increasing research interest. It is intrinsically a multi-view and semi-supervised application, since web pages usually contain two or more types of data, such a text, hyperlinks and images, and unlabeled pages are generally much more than labeled ones. Web page data is commonly high-dimensional. Thus, how to extract useful features from this kind of data in the multi-view semi-supervised scenario is important for web page classification. To our knowledge, only one method is specially presented for this topic. And with respect to a few semi-supervised multi-view feature extraction methods on other applications, there still exists much room for improvement. In this paper, we firstly design a feature extraction schema called semi-supervised intra-view and inter-view manifold discriminant (SI2MD) learning, which sufficiently utilizes the intra-view and inter-view discriminant information of labeled samples and the local neighborhood structures of unlabeled samples. We then design a semi-supervised uncorrelation constraint for the SI2MD schema to remove the multi-view correlation in the semi-supervised scenario. By combining the SI2MD schema with the constraint, we propose an uncorrelated semi-supervised intra-view and inter-view manifold discriminant (USI2MD) learning approach for web page classification. Experiments on public web page databases validate the proposed approach.","doi":null,"url":"https://ijcai.org/proceedings/2015/319","pdf":"https://ijcai.org/proceedings/2015/0319.pdf","markdown":"https://ijcai.org/proceedings/2015/319.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/319","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Web and Knowledge-Based Information Systems","track":"Main Track","number":319,"pages":{"first":2255,"last":2260},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}