{"54803":{"#nid":"54803","#data":{"type":"event","title":"Manifold Learning: Discovering Nonlinear Variation Patterns in Complex Data Sets","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE:\u003C\/strong\u003E Manifold Learning: Discovering Nonlinear Variation Patterns in \nComplex Data Sets\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESPEAKER:\u003C\/strong\u003E Professor Daniel Apley\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EIn statistical analysis and data mining of multivariate data sets, many \nproblems can be viewed as discovering variation patterns in a set of N \nobservations of n variables. The term \u0022variation pattern\u0022 refers to the \nstructured, interdependent manner in which the n variables may vary over \nthe N observations. In a very general mathematical representation we \nview each multivariate observation as a vector in n-dimensional space. \nThen over the set of N observations, we assume the data consist of a \nstructured component plus noise, where the structured component lies on \na p-dimensional manifold with\np \u0026lt;\u0026lt; n. The objective is to learn, or discover, the manifold based only \non the set of data, with no prior knowledge of what to expect. Discovery \nof the manifold is useful in many different contexts:\u0026nbsp; Denoising noisy \nimages and other multivariate data; dimensionality reduction of large \ndata sets; extraction of important features for enhancing subsequent \nanalyses; exploratory analyses for identifying and understanding \nrelationships between variables; etc. In this talk, I will discuss the \nmanifold learning problem, applications, and algorithms. Linear \nstructured manifolds can be easily discovered with standard principal \ncomponents and factor analyses. Consequently, this talk will focus on \ndiscovering nonlinear manifolds, which is a much more challenging and \nnuanced problem.\u003C\/p\u003E\u003Cp\u003EBio:\u0026nbsp; Daniel W. Apley is an Associate Professor of Industrial Engineering \u0026amp; \nManagement Sciences at Northwestern University. His research interests \nlie at the interface of engineering modeling, statistical analysis, and \ndata mining, with particular emphasis on manufacturing variation \nreduction applications in which very large amounts of data are \navailable. His research has been supported by numerous industries and \ngovernment agencies. He received the NSF CAREER award in 2001, the IIE \nTransactions Best Paper Award in 2003, and the Wilcoxon Prize for best \npractical application paper appearing in Technometrics in 2008. He \ncurrently serves as Editor-in-Chief for the Journal of Quality \nTechnology and has served as Chair of the Quality, Statistics \u0026amp; \nReliability Section of INFORMS, Director of the Manufacturing and Design \nEngineering Program at Northwestern, and Associate Editor for \nTechnometrics.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EManifold Learning: Discovering Nonlinear Variation Patterns in \nComplex Data Sets\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Manifold Learning: Discovering Nonlinear Variation Patterns in Complex Data Sets"}],"uid":"27187","created_gmt":"2010-03-08 11:19:09","changed_gmt":"2016-10-08 01:50:57","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2010-03-12T11:00:00-05:00","event_time_end":"2010-03-12T12:00:00-05:00","event_time_end_last":"2010-03-12T12:00:00-05:00","gmt_time_start":"2010-03-12 16:00:00","gmt_time_end":"2010-03-12 17:00:00","gmt_time_end_last":"2010-03-12 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}