A Convex Algorithm for Mixed Linear Regression

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dc.contributor.advisorHand, Paul E
dc.creatorJoshi, Babhru
dc.date.accessioned2017-08-01T15:18:04Z
dc.date.available2017-08-01T15:18:04Z
dc.date.created2016-12
dc.date.issued2017-03-22
dc.date.submittedDecember 2016
dc.date.updated2017-08-01T15:18:04Z
dc.description.abstractMixed linear regression is a high dimensional affine space clustering problem where the goal is to find the parameters of multiple affine spaces that best fit a collection of points. We introduce a convex 2nd order cone program (based on l1/fused lasso) which allows us to reformulate the mixed linear regression as an Rd clustering problem. The convex program is parameter free and does not require prior knowledge of the number of clusters, which is more tractable while clustering in Rd. In the noiseless case, we prove that the convex program recovers the regression coefficients exactly under narrow technical conditions of well-separation and balance. We demonstrate numerical performance on BikeShare data and music tone perception data.
dc.format.mimetypeapplication/pdf
dc.identifier.citationJoshi, Babhru. "A Convex Algorithm for Mixed Linear Regression." (2017) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/95962">https://hdl.handle.net/1911/95962</a>.
dc.identifier.urihttps://hdl.handle.net/1911/95962
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectmixed linear regression
dc.subjectmixed regression
dc.subjectmixture model
dc.subjectfused lasso
dc.titleA Convex Algorithm for Mixed Linear Regression
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputational and Applied Mathematics
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Arts
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