Combining Disparate Information for Machine Learning.
dc.contributor.author | Hsiao, Ko-Jen | en_US |
dc.date.accessioned | 2014-10-13T18:19:44Z | |
dc.date.available | NO_RESTRICTION | en_US |
dc.date.available | 2014-10-13T18:19:44Z | |
dc.date.issued | 2014 | en_US |
dc.date.submitted | 2014 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/108878 | |
dc.description.abstract | This thesis considers information fusion for four different types of machine learning problems: anomaly detection, information retrieval, collaborative filtering and structure learning for time series, and focuses on a common theme -- the benefit to combining disparate information resulting in improved algorithm performance. In this dissertation, several new algorithms and applications to real-world datasets are presented. In Chapter II, a novel approach called Pareto Depth Analysis (PDA) is proposed for combining different dissimilarity metrics for anomaly detection. PDA is applied to video-based anomaly detection of pedestrian trajectories. Following a similar idea, in Chapter III we propose to use a similar Pareto Front method for a multiple-query information retrieval problem when different queries represent different semantic concepts. Pareto Front information retrieval is applied to multiple query image retrieval. In Chapter IV, we extend a recently proposed collaborative retrieval approach to incorporate complementary social network information, an approach we call Social Collaborative Retrieval (SCR). SCR is applied to a music recommendation system that combines both user history and friendship network information to improve recall and weighted recall performance. In Chapter V, we propose a framework that combines time series data at different time scales and offsets for more accurate estimation of multiple precision matrices. We propose a general fused graphical lasso approach to jointly estimate these precision matrices. The framework is applied to modeling financial time series data. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Anomaly Detection | en_US |
dc.subject | Information Retrieval | en_US |
dc.subject | Collaborative Retrieval | en_US |
dc.subject | Inverse Covariance Matrix Estimation | en_US |
dc.title | Combining Disparate Information for Machine Learning. | en_US |
dc.type | Thesis | en_US |
dc.description.thesisdegreename | PhD | en_US |
dc.description.thesisdegreediscipline | Electrical Engineering: Systems | en_US |
dc.description.thesisdegreegrantor | University of Michigan, Horace H. Rackham School of Graduate Studies | en_US |
dc.contributor.committeemember | Hero Iii, Alfred O. | en_US |
dc.contributor.committeemember | Lee, Honglak | en_US |
dc.contributor.committeemember | Balzano, Laura Kathryn | en_US |
dc.contributor.committeemember | Savarese, Silvio | en_US |
dc.subject.hlbsecondlevel | Electrical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/108878/1/coolmark_1.pdf | |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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