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  • PP: Extracting statisticla graph features for accurate and efficient time series classification

    Problem: TSC, time series classification;

    Traditional TSC: find global similarities or local patterns/subsequence(shapelet). 

    We extract statistical features from VG to facilitate TSC

    Introduction: 

    Global similarity:

    the difference between TSC and other classification: deal with sequentiality property. 

    traditional methods: K-NN algorithm + DTW, one intrinsic issue with DTW, is that it focuses on finding global similarities. 在我看来这句话,简直是boo shit,一个距离测量只关注与全局的相似度?它应该是全部的距离都包含。

    Local features:

    Bag-of-patterns; SAX-VSM; shapelets-based algorithms. 

    Suffering:

    1. high computation complexity
    2. suboptimal classification accuracy

    Time series --------> VG --------> graph features

    graph features: Motif distribution, density; 

    Q:

    1. why it's called multiscale  VG
    2. the statistical graph features: probability distributions of small motifs, assortativity and degree statistics. 

    much faster than Learning Shapelets and Fast Shapelet. 

    Future work: 

    1. Other useful and efficient graph features: degree distribution entropy, centrality, bipartivity, etc. 

    2. adopt MVG for multivariate TSC. 

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  • 原文地址:https://www.cnblogs.com/dulun/p/12324002.html
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