{"type":"ScholarlyArticle","title":"Bayesian Extreme Components Analysis","authors":[{"name":"Yutian Chen","slug":"yutian-chen","given":"Yutian","family":"Chen","affiliation":""},{"name":"Max Welling","slug":"max-welling","given":"Max","family":"Welling","affiliation":""}],"abstract":"Extreme Components Analysis (XCA) is a statistical method based on a single eigenvalue decomposition to recover the optimal combination of principal and minor components in the data. Unfortunately, minor components are notoriously sensitive to overfitting when the number of data items is small relative to the number of attributes. We present a Bayesian extension of XCA by introducing a conjugate prior for the parameters of the XCA model. This Bayesian-XCA is shown to outperform plain vanilla XCA as well as Bayesian-PCA and XCA based on a frequentist correction to the sample spectrum. Moreover, we show that minor components are only picked when they represent genuine constraints in the data, even for very small sample sizes. An extension to mixtures of Bayesian XCA models is also explored.Yutian Chen, Max Welling","doi":null,"url":"https://ijcai.org/proceedings/2009/173","pdf":"https://ijcai.org/proceedings/2009/0173.pdf","markdown":"https://ijcai.org/proceedings/2009/173.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/173","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":173,"pages":{"first":1022,"last":1026},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}