TY - JOUR T1 - Robust Signal Identification for Dynamic Pattern Classification JF - 2016 23rd International Conference on Pattern Recognition Y1 - 2016 A1 - Zhao, Rui A1 - Schalk, Gerwin A1 - Ji, Qiang KW - computational modeling KW - data models KW - Hidden Markov models KW - motion segmentation KW - robustness KW - testing KW - Time series analysis AB - This paper addresses the problem of identifying signals of interest from discrete-time sequences contaminated by erroneous segments, which we define as the part of time series whose dynamic patterns are inconsistent with that of the signals. Assuming the signals of interest consist of consecutive samples with arbitrary starting point, duration and following a stationary dynamic pattern, we propose a robust algorithm combining Random Sample Consensus (RANSAC) and Hidden Markov Model (HMM) to automatically identify the start and end of signals of interest from time series. To evaluate the identification quality, we perform a classification task, where the identified signals are used to train a classifier. A majority vote strategy is adopted to handle error contaminated testing sequences. Compared with manual selection approach and other unsupervised learning methods, the proposed method shows improvement in classification accuracy on both synthetic and real Electrocorticographic (ECoG) data. UR - https://ieeexplore.ieee.org/document/7900245/ ER -