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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data
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AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data

机译：AdOn HDP-HMM：用于序列数据细分和分类的自适应在线模型

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摘要

Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive “learning rate” that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.
机译：近年来，见证了对自动分类数据（例如日常生活，社交媒体互动，财务系列等）的自动分类的需求日益增长。随着新数据的不断流动，至关重要的是对观察进行动态分类，而不受预定类别的限制。此外，模型应该能够响应类分布中的可能变化来更新其参数。但是，由于大多数研究都集中在预定义类集上的离线分类，因此在文献中似乎没有充分解决这个引人注目的问题。在本文中，我们基于马尔可夫切换模型和分层Dirichlet过程先验，基于自适应在线系统，提出了针对该问题的原则性解决方案。这种自适应在线方法能够在不限数量的类中对顺序数据进行分类，同时满足流上下文所特有的内存和延迟约束。在本文中，我们介绍了一种自适应的“学习率”，它负责平衡模型保留其先前参数或适应新观察结果的程度。在固定和不断发展的合成数据以及两个视频数据集（TUM Assistant Kitchen和整理的Weizmann）上的实验结果显示出在分割和分类方面的出色表现，尤其是对于来自进化分布的序列和/或包含先前未见类别的序列。

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