A Future of Abundant Sparsity: Novel Use and Analysis of Sparse Coding in Machine Learning Applications

DSpace/Manakin Repository

A Future of Abundant Sparsity: Novel Use and Analysis of Sparse Coding in Machine Learning Applications

Show simple item record

dc.contributor.author Comiter, Marcus Zachary en_US
dc.date.accessioned 2015-07-16T16:26:21Z
dc.date.created 2015-05 en_US
dc.date.issued 2015-06-26 en_US
dc.date.submitted 2015 en_US
dc.identifier.citation Comiter, Marcus Zachary. 2015. A Future of Abundant Sparsity: Novel Use and Analysis of Sparse Coding in Machine Learning Applications. Bachelor's thesis, Harvard College. en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:17417575
dc.description.abstract We present novel applications and analysis of the use of sparse coding within the con- text of machine learning. We first present Sparse Coding Trees (SC-trees), a sparse coding-based framework for resolving classification conflicts, which occur when different classes are mapped to similar feature representations. More specifically, SC-trees are novel supervised hierarchical clustering trees that use node specific dictionary and classifier training to direct input images based on classification results in the feature space at each node. We validate SC-trees on image-based emotion classification, combining it with Mirrored Nonnegative Sparse Coding (MNNSC), a novel sparse coding algorithm leveraging a nonnegativity constraint and the inherent symmetry of the domain, to achieve results exceeding or competitive with the state-of-the-art. We next present SILQ, a sparse coding-based link state model that can predictively buffer packets during wireless link outages to avoid disruption to higher layer protocols such as TCP. We demonstrate empirically that SILQ increases TCP throughput by a factor of 2-4x in varied scenarios. en_US
dc.description.sponsorship Computer Science en_US
dc.format.mimetype application/pdf en_US
dc.language.iso en en_US
dash.license META_ONLY en_US
dc.subject Computer Science en_US
dc.subject Statistics en_US
dc.title A Future of Abundant Sparsity: Novel Use and Analysis of Sparse Coding in Machine Learning Applications en_US
dc.type Thesis or Dissertation en_US
dash.depositing.author Comiter, Marcus Zachary en_US
dash.embargo.until 10000-01-01
thesis.degree.date 2015 en_US
thesis.degree.grantor Harvard College en_US
thesis.degree.level Undergraduate en_US
thesis.degree.name AB en_US
dc.type.material text en_US
thesis.degree.department Computer Science en_US
dc.identifier.orcid ORCID  0000-0002-5128-3077 en_US
thesis.degree.department-secondary Statistics en_US

Files in this item

Files Size Format View
COMITER-SENIORTHESIS-2015.pdf 16.06Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

 
 

Search DASH


Advanced Search
 
 

Submitters