title: Targeted projection pursuit for visualizing gene expression data classifications.
abstract: We present a novel method for finding low-dimensional views of high-dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced; one version based on Procrustes analysis and one based on an artificial neural network. These versions are capable of finding orthogonal or non-orthogonal projections, respectively. The method is quantitatively and qualitatively compared with other dimension reduction techniques. It is shown to find 2D views that display the classification of cancers from gene expression data with a visual separation equal to, or better than, existing dimension reduction techniques.
  
  
  BACKGROUND
  source code, additional diagrams, and original data are available from http://computing.unn.ac.uk/staff/CGJF1/tpp/bioinf.html
authors.affiliation: Northumbria University Newcastle, UK. joe.faith@unn.ac.uk
authors.forename: Joe
authors.initials: J
authors.lastname: Faith
authors.forename: Robert
authors.initials: R
authors.lastname: Mintram
authors.forename: Maia
authors.initials: M
authors.lastname: Angelova
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D003628:Database Management Systems; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014584:User-Computer Interface