Annealing-based model-free expectation maximisation for multi-colour flow cytometry data clustering
Year: 2015
Type: article
Abstract: This paper proposes an optimised model-free expectation maximisation method for automated clustering of high-dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carried out using a model-free expectation maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions are continued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-colour flow cytometry datasets showed that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters or their distribution models.
Authors: Başak Esin Köktürk, Bilge Karaçalı
Institution: Izmir Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2201595269