Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Tensor fusion: learning in heterogeneous and distributed data

Basit öğe kaydını göster

dc.contributor Ph.D. Program in Computer Engineering.
dc.contributor.advisor Cemgil, Ali Taylan.
dc.contributor.author Şimşekli, Umut.
dc.date.accessioned 2023-03-16T10:13:44Z
dc.date.available 2023-03-16T10:13:44Z
dc.date.issued 2015.
dc.identifier.other CMPE 2015 S56 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12603
dc.description.abstract In this thesis, we focus on coupled matrix and tensor factorization models; that provide a good modeling accuracy { practicality trade o for modeling large-scale and/or heterogeneous data that are collected from diverse sources. Our main concern in this thesis will be to develop inference methods for coupled tensor factorization models. We will rst develop a rigorous tensor factorization notation, that aims to cover all possible model topologies and coupled factorization models. Our notation highlights the partially separable structure of tensor factorization models, which paves the way for developing parallel and distributed inference algorithms. Secondly, we will develop novel methods for making inference in coupled tensor factorization models. The proposed methods can be separated into three groups. In the rst set of methods, we will focus on optimization-based approaches for making maximum likelihood and a-posteriori estimation of the latent variables. The second group of methods builds up on the rst group and jointly estimates the relative weights and divergence functions, which play important role in coupled factorization models. Finally, in the third group, we will focus on full Bayesian inference, where we will develop several Markov Chain Monte Carlo methods for sampling from the posterior distributions of the latent variables. We will evaluate our methods on several challenging applications. We will develop novel factorization models for addressing challenging audio processing applications. We will also evaluate our distributed inference methods on large-scale link prediction applications, where we will report successful results in all of these applications.
dc.format.extent 30 cm.
dc.publisher Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2015.
dc.subject.lcsh Diffusion tensor imaging.
dc.title Tensor fusion: learning in heterogeneous and distributed data
dc.format.pages xx, 158 leaves ;


Bu öğenin dosyaları

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster

Dijital Arşivde Ara


Göz at

Hesabım