title: Discriminative components of data.
abstract: A simple probabilistic model is introduced to generalize classical linear discriminant analysis (LDA) in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to 1) maximizing mutual information with the classes, and 2) finding principal components in the so-called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments, the method outperformed, in addition to more classical methods, a Renyi entropy-based alternative while having essentially equivalent computational cost.
authors.affiliation: Neural Networks Research Center, Helsinki University of Technology, Helsinki, FIN-02015 HUT Finland. jaakko.peltonen@hut.fi
authors.forename: Jaakko
authors.initials: J
authors.lastname: Peltonen
authors.forename: Samuel
authors.initials: S
authors.lastname: Kaski
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003205:Computing Methodologies; D016208:Databases, Factual; D016002:Discriminant Analysis; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D028662:Principle-Based Ethics