Generalized multi-view data proliferator (gem-vip) for boosting classification

dc.contributor.advisor Rekik, Islem Çelik, Mustafa
dc.contributor.authorID 504131531
dc.contributor.department Computer Engineering 2023-11-23T07:31:28Z 2023-11-23T07:31:28Z 2022-08-08
dc.description Thesis (M.Sc.) -- Istanbul Technical University, Graduate School, 2022
dc.description.abstract Multi-view network representation revealed multi-faced alterations of the brain as a complex interconnected system, particularly in mapping neurological disorders. Such rich data representation maps the relationship between different brain views which has the potential of boosting neurological diagnostic tasks. However, multi-view brain data is scarce and generally is collected in small sizes. Thus, such data type is broadly overlooked among researchers due to its relatively small size. Despite the existence of data proliferation techniques as a way to overcome data scarcity, to the best of our knowledge, multi-view data proliferation from a single sample has not been fully explored. Here, we propose to bridge this gap by proposing our GEneralized Multi-VIew data Proliferator (GEM-VIP), a framework aiming to proliferate synthetic multi-view brain samples from a single multi-view brain to boost multi-view brain data classification tasks. For the given Connectional Brain Template (i.e., represents an approximation of brain graphs that captures the unique connection shared by a population's subjects), we set out the proliferate synthetic multi-view brain graphs using the inverse of multi-variate normal distribution (MVND). However, one needs two crucial components, which are the mean an the covariance of a given population. As such, first, our proposed GEM-VIP framework obtains a population-representative tensor (i.e., drawn from the prior CBT) which can be mathematically regarded as a mean of the population. Second, drawing inspiration from the genetic algorithm paradigm our proposed GEM-VIP learns the covariance matrix of the population using the given CBT. Lastly, it proliferates synthetic samples using the earlier obtained representative tensor and created covariance matrix of the population on the MVND equation. We evaluate our GEM-VIP against several comparison methods. The results show that our framework boosts the multi-view brain data classification accuracy of AD/ lMCI and eMCI/ normal control (NC) datasets. In short, our GEM-VIP method boosts the diagnoses of the neurological disorders. M.Sc.
dc.language.iso en_US
dc.publisher Graduate School
dc.sdg.type none
dc.subject Information organization
dc.subject Bilgi organizasyonu
dc.subject Data structures
dc.subject Veri yapıları
dc.title Generalized multi-view data proliferator (gem-vip) for boosting classification
dc.title.alternative Genelleştirilmiş çok boyutlu veri üretimi ile sınıflandırma hassaslığının yükseltilmesi
dc.type Master Thesis
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