Multiview: a software package for multiview pattern recognition methods
2018
Multiview datasets are the norm in bioinformatics, often under the label multi-omics. Multiview
data is gathered from several experiments, measurements or feature sets available for the same subjects.
Recent studies in pattern recognition have shown the advantage of using multiview methods of clustering
and dimensionality reduction; however, none of these methods are readily available to the extent of our
knowledge. Multiview extensions of four well-known pattern recognition methods are proposed here. Three
multiview dimensionality reduction methods: multiview t-distributed stochastic neighbour embedding,
multiview multidimensional scaling, and multiview minimum curvilinearity embedding, as well as a multiview
spectral clustering method. Often they produce better results than their single-view counterparts, tested
here on four multiview datasets.
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