title: High-dimensional cluster analysis with the masked EM algorithm.
abstract: Cluster analysis faces two problems in high dimensions: the "curse of dimensionality" that can lead to overfitting and poor generalization performance and the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of spike sorting for next-generation, high-channel-count neural probes. In this problem, only a small subset of features provides information about the cluster membership of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a "masked EM" algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data and to real-world high-channel-count spike sorting data.
authors.affiliation: UCL Institute of Neurology and UCL Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6DE, U.K. s.kadir@ucl.ac.uk.
authors.forename: Shabnam N
authors.initials: SN
authors.lastname: Kadir
authors.forename: Dan F M
authors.initials: DF
authors.lastname: Goodman
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Harris
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D008959:Models, Neurological; D008962:Models, Theoretical; D009474:Neurons / Q000502:physiology*