Matrix factorisation (MF) techniques are based on approximating a high dimension matrix A (original data) by a product of two or more lower dimension matrices.
They are generally referred to as matrix decomposition or matrix factorization techniques.
The MF techniques found in these key-frame extraction methods include singular value decomposition (SVD) and non-negative matrix factorisation.
Mandula, O., Šestak, I.Š., Heintzmann, R. & Williams, C.K. Localisation microscopy with quantum dots using non-negative matrix factorisation.
(b) Non-negative matrix factorisation (NMF) consensus clustering identified four molecular subgroups (WNT, SHH, Grp3 and Grp4), defined by four metagenes.
We saw a broad range of approaches, from generalised boosted methods to random forests, single value decomposition to matrix factorisation and collaborative filtering, with no one class of model outperforming all the others.
Last but not least, both principal component analysis and positive matrix factorisation were conducted on 37 samples to apportion pollutant sources in harvested rainwater.
Non-negative matrix factorisation (NMF) consensus clustering13,19,20 was used to identify subgroup membership of a training cohort comprising genome-scale Illumina 450k DNA methylation microarray data for 220 medulloblastomas (Fig. 1b; Fig. 2).
Non-negative matrix factorisation (NMF) consensus clustering19 was used to identify the four recognised medulloblastoma consensus molecular subgroups13 using 220 training cohort samples run on the Illumina 450k methylation microarray platform (Fig. 2).
The results from principal component analysis and positive matrix factorisation were in agreement, although the latter identified mains water top-up as an additional factor responsible for dissolved solids.
