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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Infinite Hidden Conditional Random Fields for Human Behavior Analysis
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Infinite Hidden Conditional Random Fields for Human Behavior Analysis

机译：用于人类行为分析的无限隐藏条件随机场

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摘要

Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). In this brief, we present the infinite HCRF (iHCRF), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automatically learning the optimal number of hidden states for a classification task. We show how we learn the model hyperparameters with an effective Markov-chain Monte Carlo sampling technique, and we explain the process that underlines our iHCRF model with the Restaurant Franchise Rating Agencies analogy. We show that the iHCRF is able to converge to a correct number of represented hidden states, and outperforms the best finite HCRFs—chosen via cross-validation—for the difficult tasks of recognizing instances of agreement, disagreement, and pain. Moreover, the iHCRF manages to achieve this performance in significantly less total training, validation, and testing time.
机译：隐藏条件随机字段（HCRF）是具有判别性的潜在变量模型，已被证明可以成功地学习给定分类问题的隐藏结构（提供对隐藏状态数的适当验证）。在本文中，我们介绍了无限HCRF（iHCRF），它是一种基于分层Dirichlet流程的非参数模型，能够自动学习分类任务的最佳隐藏状态数。我们将展示如何使用有效的马尔可夫链蒙特卡洛采样技术来学习模型超参数，并通过与餐厅特许经营评级机构进行类比来说明支持我们的iHCRF模型的过程。我们表明，iHCRF能够收敛到正确数量的表示的隐藏状态，并且在识别共识，分歧和痛苦的艰巨任务方面胜过通过交叉验证选择的最佳有限HCRF。此外，iHCRF可以通过显着减少总的培训，验证和测试时间来实现这一性能。

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