Work

Segmentation of binary sequence via minimizing least square error with total variation regularization

Year: 2024

Type: article

Abstract: In this paper, we propose a data-driven procedure to segment a binary sequence as an alternative to the popular hidden Markov model (HMM) based procedure.Unlike the HMM, our procedure does not make any distributional or model assumption to the data.To segment the sequence, we suggest to minimize the least square distance from the observations under total variation regularization to the solution, and develop a polynomial time algorithm for it.Finally, we illustrate the algorithm using a toy example and apply it to the Gemini boat race data between Oxford and Cambridge University.Further, we numerically compare the performance of our procedure to the HMM based segmentation through these examples.

Source: Communications for Statistical Applications and Methods

DOI: 10.29220/csam.2024.31.5.487

Authors: Jeungju Kim, Johan Lim

Institution: Seoul National University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Research Foundation, National Research Foundation of Korea

Awards: G154185955, G6479763254