CIF:Small: Dynamic Dictionary Learning with Low-rank Interference

到2020年，预计每个人将贡献1000 gb的传感器数据。这些传感器包括但不限于GPS、加速度计、陀螺仪、麦克风、摄像头、各种可穿戴生物医学传感器等。在监测人类活动的领域之外，多传感器数据处理在许多实际应用的背景下一直是一个活跃的研究课题，例如医学图像分析，遥感和军事目标/威胁检测。解决这些关键的大数据问题的一个强大工具是稀疏驱动的信号处理技术。解析表示不仅为带宽/存储效率提供更好的信号压缩，而且还导致更快的处理算法以及更有效的信号分离，用于检测，分类和识别目的，因为它专注于数据的最内在属性。稀疏信号表示允许捕获数据丛林中隐藏的简化结构，从而在实际设置中最大限度地减少噪声的有害影响。本文研究了动态字典学习的问题，以便在存在低秩干扰的情况下获得最有效和自适应的稀疏数据表示。研究者试图开发一个通用框架，利用多样性，但互补，从多个（可能是异构的）传感器收集的大量相关数据源的特征，这些数据源位于相同的时空物理空间中，记录相同的物理事件。这种情况确保干扰噪声模式非常相似，因此证明了干扰的低秩特性，同时允许在字典和/或稀疏编码中捕获感兴趣的信号的特定结构稀疏模式。

By 2020, it is projected that every human being will contribute 1,000GBof the sensor data individually. These sensors include but not limited to GPS, accelerometer, gyroscope, microphone, camera, all kinds ofwearable biomedical sensors, etc. Beyond the domain of monitoringhuman activity, multi-sensor data processing has been an activeresearch topic within the context of numerous practical applications, such as medical image analysis, remote sensing, and militarytarget/threat detection. One powerful tool to tackle these criticalBig Data problems is sparsity-driven signal-processing techniques. Asparse representation not only provides better signal compression forbandwidth/storage efficiency, but also leads to faster processingalgorithms as well as more effective signal separation for detection,classification and recognition purposes since it focuses on the mostintrinsic property of the data. Sparse signal representation allows one tocapture the hidden simplified structure present in the data jungle, andthus minimizes the harmful effects of noise in practical settings.This research investigates the problem of dynamic dictionary learning toobtain the most effective and adaptive sparse data representation evenin the presence of low-rank interference. The investigator seeks todevelop a general framework that takes advantage of diversity, yetcomplementary, features in vast correlated data sources collected frommultiple, possibly heterogeneous, sensors co-located in the same spatio-temporal physical space, recording the same physical event. Such scenarios ensure that interference noise patterns are very similar,hence justifying the low-rank property of the interference, while allowspecific structural sparsity pattern of the signal of interest to be capturedin the dictionary and/or in the sparse codes.