Abstract
To provide a parsimonious generative representation of the sequential activity of a number of individuals within a population there is a necessary tradeoff between the definition of individual specific and global representations. A linear-time algorithm is proposed that defines a distributed predictive model for finite state symbolic sequences which represent the traces of the activity of a number of individuals within a group. The algorithm is based on a straightforward generalization of latent Dirichlet allocation to time-invariant Markov chains of arbitrary order. The modelling assumption made is that the possibly heterogeneous behavior of individuals may be represented by a relatively small number of simple and common behavioral traits which may interleave randomly according to an individual-specific distribution. The results of an empirical study on three different application domains indicate that this modelling approach provides an efficient low-complexity and intuitively interpretable representation scheme which is reflected by improved prediction performance over comparable models.
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Mark Girolami is a Reader in the Department of Computing Science at the University of Glasgow. In 2000 he was the TEKES visiting professor at the Laboratory of Computing and Information Science in Helsinki University of Technology. In 1998 and 1999 Dr. Girolami was a research fellow at the Laboratory for Advanced Brain Signal Processing in the Brain Science Institute, RIKEN, Wako-Shi, Japan. He has been a visiting researcher at the Computational Neurobiology Laboratory (CNL) of the Salk Institute. This year (2005) he will take up an MRC funded Discipline Hopping Award in the Department of Biochemistry. Mark Girolami holds a degree in Mechanical Engineering from the University of Glasgow (1985), and a Ph.D. in Computing Science from the University of Paisley (1998). Dr. Girolami was a development engineer with IBM from 1985 until 1995 when he left to pursue an academic career.
Ata Kabán received the B.Sc. degree with honours (1999) in computer science from the University “Babes-Bolyai” of Cluj-Napoca, Romania, and the Ph.D. degree in computer science (2001) from the University of Paisley, UK. She is a lecturer in the School of Computer Science of the University of Birmingham. Her current interests concern probabilistic modelling, machine learning and their applications to automated data analysis. She has been a visiting researcher at Helsinki University of Technology (June–December 2000 and in the summer of 2003). Prior to her career in Computer Science, she received the B.A. degree in musical composition (1994) and the M.A. (1995) and Ph.D. (1999) degrees in musicology from the Music Academy “Gh. Dima” of Cluj-Napoca, Romania.
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Girolami, M., Kabán, A. Sequential Activity Profiling: Latent Dirichlet Allocation of Markov Chains. Data Min Knowl Disc 10, 175–196 (2005). https://doi.org/10.1007/s10618-005-0362-2
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DOI: https://doi.org/10.1007/s10618-005-0362-2