Data visualization and dimensionality reduction using kernel maps with a reference point.
In this paper, a new kernel-based method for data visualization and dimensionality reduction is proposed. A reference point is considered corresponding to additional constraints taken in the problem formulation. In contrast with the class of kernel eigenmap methods, the solution (coordinates in the low-dimensional space) is characterized by a linear system instead of an eigenvalue problem. The kernel maps with a reference point are generated from a least squares support vector machine (LS-SVM) core part that is extended with an additional regularization term for preserving local mutual distances together with reference point constraints. The kernel maps possess primal and dual model representations and provide out-of-sample extensions, e.g., for validation-based tuning. The method is illustrated on toy problems and real-life data sets.
Suykens
Johan A K
JA
Katholieke Universiteit Leuven, ESAT-SCD/SISTA,B-3001 Leuven, Heverlee, Belgium. johan.suykens@esat.kuleuven
D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D003628:Database Management Systems; D016208:Databases, Factual; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D014584:User-Computer Interface