How an HMM Can Improve Unsupervised Learning

Hidden Markov Models (HMM) can significantly enhance the understanding of unsupervised learning by inferring the internal states of subjects, such as animals in behavioral research, without prior labeling of those states. Unlike traditional unsupervised techniques that primarily focus on clustering—which can vary widely in approach—HMMs provide a structured methodology to deduce hidden layers of information from observational data. This approach facilitates comparative analysis between different categories of subjects, yielding insights into underlying behavioral patterns that may not be readily apparent. By using HMMs, researchers can derive meaningful interpretations of data that contribute to a deeper understanding of dynamic systems in various biological contexts.