Matrix Factorization

Count Matrix Factorization for Dimension Reduction and Data Visualization

For nearly 20 years, sequencing technologies have been on the rise, producing more and more data often characterized by their high dimensionality, meaning when the number $p$ of covariates like genes is far larger than the number $n$ of observations. Analysing such data is a statistical challenge and requires the use of dimension reduction approaches. Compression methods show particular abilities concerning data interpretation through visualisation or for clustering. Especially, projection-based methods such as principal component analysis (PCA) generally solve a problem of matrix factorization, for instance the PCA corresponds to a singular value decomposition (SVD).