Show simple item record

dc.contributor.authorKoplik, Peter Sebastian
dc.date.accessioned2019-02-12T14:42:58Z
dc.date.available2019-02-12T14:42:58Z
dc.date.created2018-12
dc.date.submittedDecember 2018
dc.identifier.urihttp://hdl.handle.net/1853/60887
dc.description.abstractWe explore the effectiveness of the random projection method, a biologically plausible, computationally efficient,and data-independentmethodofdimensionalityreductionindistinctionbetweencategoriesofvisualstimuli. We observe that a neural network tasked with approximating the original stimulus from the reduced domain generally excludes information not useful in distinguishing visual categories. This suggests that random projection may be useful in the efficient recall and recognition of visual concepts even though the projections only contain small fractions of the original information. Our findings indicate that the reconstruction of visual stimuli from the random projected domain preserves best the features most typical of that particular category of stimuli.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectRandom projection
dc.subjectNeural network
dc.subjectConcept learning
dc.titleA Mechanism of Visual Concept Learning via Neuronal Random Projection
dc.typeUndergraduate Research Option Thesis
dc.description.degreeUndergraduate
dc.contributor.departmentComputer Science
thesis.degree.levelUndergraduate
dc.contributor.committeeMemberArriaga, Rosa I.
dc.contributor.committeeMemberVempala, Santosh S.
dc.date.updated2019-02-12T14:42:58Z


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record