One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, including video classification, action recognition, and text translation. The recently developed generalized Dirichlet HMMs have proven efficient in proportional sequential data modeling. As such, we focus on investigating a maximum a posteriori (MAP) framework for the inference of its parameters. The proposed approach differs from the widely deployed Baum–Welch through the placement of priors that regularizes the estimation process. A feature selection paradigm is also integrated simultaneously in the algorithm. For validation, we apply our proposed approach in the classification of dynamic textures and the recognition of infrared actions.

中文翻译：

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具有同时特征选择的比例序列数据建模的隐马尔可夫模型的最大后验近似

时间序列数据研究和分析中的支柱生成机器学习方法之一是隐马尔可夫模型 (HMM)。早期的研究主要集中在该模型的语音识别应用，后来扩展到许多领域，包括视频分类、动作识别和文本翻译。最近开发的广义 Dirichlet HMM 已被证明在比例顺序数据建模方面是有效的。因此，我们专注于研究用于推断其参数的最大后验 (MAP) 框架。所提出的方法与广泛部署的 Baum-Welch 不同，它通过放置使估计过程规范化的先验来实现。算法中还同时集成了特征选择范式。为了验证，