Читать книгу Informatics and Machine Learning. From Martingales to Metaheuristics онлайн

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The prospect of having a HMM feature extraction in the streaming signal processing pipeline (O(L), for size L data process) offers powerful real‐time feature extraction capabilities and specialized filtering (all of which is implemented in the Nanoscope, ssss1). One such processing method, described in ssss1, is HMM/Expectation Maximization (EM) EVA (Emission Variance Amplification) Projection which has application in providing simplified automated tFSA Kinetic Feature Extraction from channel current signal. What is needed is the equivalent of low‐pass filtering on blockade levels while retaining sharpness on the timing of the level changes. This is not possible with the standard low‐pass filter because the edges get blurred out in the local filtering process, but notice how this does not happen with the HMM‐based filter, for the data shown in ssss1.

HMM is a common intrinsic statistical sequence modeling method (implementations and applications are mainly drawn from [135–158] in what follows), so the question naturally arises – how to optimally incorporate extrinsic “side‐information” into a HMM? This can be done by treating duration distribution information itself as side‐information and a process is shown for incorporating side‐information into a HMM. It is thereby demonstrated how to bootstrap from a HMM to a HMMD (more generally, a hidden semi‐Markov model or HSMM, as it will be described in ssss1).