Simple integrative preprocessing preserves what is shared in data sources

Background: Bioinformatics data analysis toolbox needs general-purpose, fast and easily interpretable preprocessing tools that perform data integration during exploratory data analysis. Our focus is on vector-valued data sources, each consisting of measurements of the same entity but on different variables, and on tasks where source-specific variation is considered noisy or not interesting. Principal components analysis of all sources combined together is an obvious choice if it is not important to distinguish between data source-specific and shared variation. Canonical Correlation Analysis (CCA) focuses on mutual dependencies and discards source-specific "noise" but it produces a separate set of components for each source. Results: It turns out that components given by CCA can be combined easily to produce a linear and hence fast and easily interpretable feature extraction method. The method fuses together several sources, such that the properties they share are preserved. ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Abhishek Tripathi, Arto Klami, Samuel Kaski
• Keywords: Analysis, BMCBI 2008, Exploratory Data Analysis, Vector-valued Data Sources