Knowledge-constrained projection of high-dimensional data

Amra Omanović (2018) Knowledge-constrained projection of high-dimensional data. MSc thesis.

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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.

Item Type:

Thesis (MSc thesis)

Keywords:

data projection, latent spaces, regularization, data science, single-cell genomics

Number of Pages:

Language of Content:

English

Mentor / Comentors:

Name and Surname	ID	Function
izr. prof. dr. Polona Oblak	6760	Mentor
prof. dr. Blaž Zupan	106	Comentor

Link to COBISS:

http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537930947)

Institution:

University of Ljubljana

Department:

Faculty of Computer and Information Science

Item ID:

4221

Date Deposited:

13 Sep 2018 17:55

Last Modified:

27 Sep 2018 13:26

URI:

http://eprints.fri.uni-lj.si/id/eprint/4221

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