Compact representation of temporal processes in echosounder time series via matrix decomposition
Compact representation of temporal processes in echosounder time series via matrix decomposition
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DOI：
10.1121/10.0002670
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发表时间：
2020-07
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影响因子：
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通讯作者：
Wu-Jung Lee;Valentina Staneva
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文献类型：
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作者：
Wu-Jung Lee;Valentina Staneva
最近从不同海洋平台获得的回声探测仪数据的爆炸性增长，创造了在大范围内观察海洋生态系统的前所未有的机会。然而，严重缺乏能够自动发现和总结突出的时空超声结构的方法，限制了这些丰富的数据集的有效和更广泛的使用。为了应对这一挑战，开发了一种基于矩阵分解的数据驱动方法，该方法利用数据中的内在特征来构建长期回声测深仪时间序列的紧凑表示。在两阶段方法中，首先通过主成分追踪从数据中去除噪声离群点，然后使用时间平滑的非负矩阵因式分解来自动发现少量不同的日常超声模式，这些模式的时变线性组合(激活)重建了主要的超声图像结构。这种低阶表示提供了比原始数据更易于处理和解释的生物信息，适合于与其他海洋变量一起进行可视化和系统分析。与依赖固定的、手工制定的规则的现有方法不同，这种无监督的机器学习方法非常适合从从不熟悉或快速变化的生态系统收集的数据中提取信息。这项工作为构建基于声学的大规模海洋生物观测的稳健时间序列分析奠定了基础。
The recent explosion in the availability of echosounder data from diverse ocean platforms has created unprecedented opportunities to observe the marine ecosystems at broad scales. However, the critical lack of methods capable of automatically discovering and summarizing prominent spatio-temporal echogram structures has limited the effective and wider use of these rich datasets. To address this challenge, a data-driven methodology is developed based on matrix decomposition that builds compact representation of long-term echosounder time series using intrinsic features in the data. In a two-stage approach, noisy outliers are first removed from the data by principal component pursuit, then a temporally smooth nonnegative matrix factorization is employed to automatically discover a small number of distinct daily echogram patterns, whose time-varying linear combination (activation) reconstructs the dominant echogram structures. This low-rank representation provides biological information that is more tractable and interpretable than the original data, and is suitable for visualization and systematic analysis with other ocean variables. Unlike existing methods that rely on fixed, handcrafted rules, this unsupervised machine learning approach is well-suited for extracting information from data collected from unfamiliar or rapidly changing ecosystems. This work forms the basis for constructing robust time series analytics for large-scale, acoustics-based biological observation in the ocean.