Hansen, Toke Jansen

I løbet af de seneste årtier er computere kommet til at spille en væsentlig rolle i vores daglige liv, og data bliver nu indsamlet i forskellige domæner i et hurtigere tempo end nogensinde før. Denne afhandling præsenterer forskningsresultater i fire overordnede maskinlæringsfelter, som alle vedrører de udfordringer der ligger i analysen af store datasæt.Inden for kernelmetoder præsenterer vi en informationsbaseret støjreduktionsteknik baseret på semi-supervised kernel principal komponentanalyse [Principal Component Analysis (PCA)], som inkorporerer sideinformationer omkring data. Effektivt guider dette lavrangsrepræsentationen mod komponenter, der er relevante, og samtidig udnyttes eksponerede glatte strukturer i data. Inden for samme felt præsenterer vi en skalerbar randomiseret heuristisk til at optimere kernel hyperparametre, der er baseret på maksimering af den mindste omsluttende kugle [Minimum Enclosing Ball (MEB)] udspændt af gennemsnittet for individuelle klassestrukturer, hvor de underlæggende data er repræsenteret i et reproducerende kernel Hilbert rum [Reproducing Kernel Hilbert Space (RKHS)].Inden for spektrale metoder introducerer vi semi-supervised egenvektorer for en graf Laplacian, der arver mange af de egenskaber, der kendetegner de globale egenvektorer, men ved at inkorporere sideinformationer i optimeringsproblemet, er semi-supervised egenvektorer bedre til at modelere lokale heterogeniteter.Inden for maskinlæring i neuroimaging introducerer vi maskinlæringsprotokoller for realtids funktionel magnetisk resonans [functional Magnetic Resonance Imaging (fMRI)], der giver mulighed for dynamiske indgreb i den menneskelige beslutningsproces. Specifikt udnytter modellen strukturen af fMRI data ved at inkorporere en tidslig Gausisk process [Gaussian Process (GP)] glatheds-prior, hvilket reducerer modeldegenerering forårsaget af upræcise sideinformationer.Slutteligt, inden for emnemodellering, introducerer vi et grafikkortaccelereret [Graphics Processing Unit (GPU)] system for detektion af klyngedannelser i todelte storskala netværk med sparsomme observationer. Ved at implementere den uendelige relationelle model [Infinite Relational Model (IRM)] i dette system opnår vi hastighedsforbedringer i to størrelsesordener sammenlignet med modelestimering baseret på konventionelle processorer.Over the last few decades computers have gotten to play an essential role in our daily life, and data is now being collected in various domains at a faster pace than ever before. This dissertation presents research advances in four machine learning fields that all relate to the challenges imposed by the analysis of big data.In the field of kernel methods, we present an information-based denoising technique based on semi-supervised kernel Principal Component Analysis (PCA), that incorporates label information into the kernel PCA objective. Effectively, this guides the low-rank representation towards relevant components, while exploiting intrinsic manifold structures exposed by the data. In the same field, we also introduce a scalable randomized heuristic for optimizing kernel hyperparameters, that is based on maximizing the Minimum Enclosing Ball (MEB) of the class means in the associated Reproducing Kernel Hilbert Space (RKHS).In the field of spectral methods, we introduce semi-supervised eigenvectors of a graph Laplacian, that inherit many of the properties that characterize the global eigenvectors, but by using side-information in the form of a seed set, the semi-supervised eigenvectors are better at modeling local heterogeneities.In the field of machine learning for neuroimaging, we introduce learning protocols for real-time functional Magnetic Resonance Imaging (fMRI) that allow for dynamic intervention in the human decision process. Specifically, the model exploits the structure of fMRI data by incorporating a temporal Gaussian Process (GP) smoothness prior, which reduces model degeneracy caused by mislabeled data samples.Finally, in the field of topic modeling, we introduce a Graphics Processing Unit (GPU) accelerated framework for co-clustering in large-scale sparse bipartite networks. By implementing the Infinite Relational Model (IRM) in this framework we achieve speedups of two orders of magnitude compared to estimation based on conventional processors

Online Research Database In Technology

Large-scale Machine Learning in High-dimensional Datasets

http://orbit.dtu.dk/ws/files/106929440/phd300_Hansen_TJ.pdf

Large-scale Machine Learning in High-dimensional Datasets

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Online Research Database In Technology