{"603205":{"#nid":"603205","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Bradley Whitaker","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003EModifying Sparse Coding for Imbalanced Classification\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. David Anderson, ECE, Chair , Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Justin Romberg, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Christopher Rozell, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Wing Li, Math\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Gari Clifford, Emory\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe objective of this research is to explore the use of sparse coding as a tool for unsupervised\u0026nbsp;feature learning to help in the classification of imbalanced datasets. Traditional sparse\u0026nbsp;coding dictionaries are learned by minimizing the average approximation error between a\u0026nbsp;vector and its sparse decomposition. As such, these dictionaries may overlook important\u0026nbsp;features that are only present in the minority class. Without these features, it may be difficult\u0026nbsp;to accurately classify between the two classes. To overcome this problem, this work\u0026nbsp;explores novel modifications to the dictionary learning framework that encourage dictionaries\u0026nbsp;to include anomalous features. Sparse coding also inherently assumes that a vector\u0026nbsp;can be represented as a sparse linear combination of a feature set. This work addresses the\u0026nbsp;ability of sparse coding to learn a representative dictionary when the underlying data has\u0026nbsp;a nonlinear sparse structure. Finally, this work applies the intuition gained from exploring\u0026nbsp;dictionary learning in this manner to the classification of three imbalanced datasets.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Modifying Sparse Coding for Imbalanced Classification "}],"uid":"28475","created_gmt":"2018-03-02 21:52:07","changed_gmt":"2018-03-05 14:13:29","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-03-14T17:15:00-04:00","event_time_end":"2018-03-14T19:15:00-04:00","event_time_end_last":"2018-03-14T19:15:00-04:00","gmt_time_start":"2018-03-14 21:15:00","gmt_time_end":"2018-03-14 23:15:00","gmt_time_end_last":"2018-03-14 23:15:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}