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Knowledge-constrained projection of high-dimensional data
Omanović, Amra (Author), Oblak, Polona (Mentor) More about this mentor... This link opens in a new window, Zupan, Blaž (Co-mentor)

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Abstract
Projection of high-dimensional data is usually done by reducing dimensionality of the data and transforming the data to the latent space. We created synthetic data to simulate real gene-expression datasets and we tested methods on both synthetic and real data. With this work we address the visualization of our data through implementation of regularized singular value decomposition (SVD) for biclustering using L0-norm and L1-norm. Additional knowledge is introduced to the model through regularization with the two prior adjacency matrices. We show that L0-norm SVD and L1-norm SVD give better results than standard SVD.

Language:English
Keywords:data projection, latent spaces, regularization, data science, single-cell genomics
Work type:Master's thesis/paper (mb22)
Organization:FRI - Faculty of computer and information science
Year:2018
Views:193
Downloads:214
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Secondary language

Language:Slovenian
Title:Projekcija visokodimenzionalnih podatkov ob upoštevanju domenskih omejitev
Abstract:
Projekcija visokodimenzionalnih podatkov se običajno pripravi z zmanjšanjem dimenzionalnosti, ki se predstavi v latentnem prostoru, kar omogoča smiselno vizualizacijo. Pripravili smo sintetične podatke, ki odražajo gensko izražanje v pravih podatkovnih zbirkah. Metode smo kasneje testirali na pripravljenih sintetičnih in pravih podatkih. V tem delu smo obravnavali naloge z izvajanjem regularizirane SVD metode, z uporabo L0-norme in L1-norme. Modelu je bila dodana informacija z regularizacijo dveh dodatnih matrik sosednosti. Pokazali smo, da so te metode dale boljše rezultate kot standardni SVD.

Keywords:projekcija podatkov, latentni prostori, regularizacija, podatkovna veda, genomika posameznih celic

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