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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use Patterns
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Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use Patterns

机译：稀疏分布式超维编码的序列预测在手机使用模式分析中的应用

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摘要

Modeling and prediction of temporal sequences is central to many signal processing and machine learning applications. Prediction based on sequence history is typically performed using parametric models, such as fixed-order Markov chains (n-grams), approximations of high-order Markov processes, such as mixed-order Markov models or mixtures of lagged bigram models, or with other machine learning techniques. This paper presents a method for sequence prediction based on sparse hyperdimensional coding of the sequence structure and describes how higher order temporal structures can be utilized in sparse coding in a balanced manner. The method is purely incremental, allowing real-time online learning and prediction with limited computational resources. Experiments with prediction of mobile phone use patterns, including the prediction of the next launched application, the next GPS location of the user, and the next artist played with the phone media player, reveal that the proposed method is able to capture the relevant variable-order structure from the sequences. In comparison with the n-grams and the mixed-order Markov models, the sparse hyperdimensional predictor clearly outperforms its peers in terms of unweighted average recall and achieves an equal level of weighted average recall as the mixed-order Markov chain but without the batch training of the mixed-order model.
机译：时间序列的建模和预测对于许多信号处理和机器学习应用至关重要。基于序列历史的预测通常使用参数模型执行，例如固定阶马尔可夫链（n-gram），高阶马尔可夫过程的近似值（例如混合阶马尔可夫模型或滞后二元模型的混合物）或其他机器学习技术。本文提出了一种基于序列结构的稀疏超维编码的序列预测方法，并描述了如何在平衡编码中利用高阶时间结构进行稀疏编码。该方法是纯增量式的，允许使用有限的计算资源进行实时在线学习和预测。预测手机使用模式的实验（包括预测下一个启动的应用程序，用户的下一个GPS位置以及下一个由电话媒体播放器播放的艺术家）表明，所提出的方法能够捕获相关的变量-序列的顺序结构。与n元语法和混合顺序马尔可夫模型相比，稀疏高维预测变量在未加权平均召回率方面明显优于其同类产品，并且在加权平均召回率方面与混合顺序马尔可夫链相同，但没有批量训练混合订单模型。

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