Bargi Ava, Xu Richard Yi Da, Piccardi Massimo
IEEE Trans Neural Netw Learn Syst. 2018 Sep;29(9):3953-3968. doi: 10.1109/TNNLS.2017.2742058. Epub 2017 Sep 21.
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.
近年来，对诸如日常生活活动、社交媒体互动、金融序列等序列数据进行自动分类的需求日益增加。随着新数据的不断涌现，即时对观测数据进行分类且不受预定类别数量的限制至关重要。此外，模型应能够根据类别的分布可能发生的变化来更新其参数。然而，这个紧迫的问题在文献中似乎并未得到充分解决，因为大多数研究都集中在对预定义类集进行离线分类。在本文中，我们基于利用马尔可夫切换模型和层次狄利克雷过程先验的自适应在线系统，提出了针对这个问题的原则性解决方案。这种自适应在线方法能够对无限数量的类别上的序列数据进行分类，同时满足流数据环境中典型的内存和延迟约束。在本文中，我们引入了一种自适应“学习率”，它负责平衡模型保留其先前参数或适应新观测的程度。在平稳和演化的合成数据以及两个视频数据集（TUM辅助厨房和整理后的魏茨曼数据集）上的实验结果表明，在分割和分类方面具有显著性能，特别是对于来自演化分布的序列和/或包含以前未见过类别的序列。
