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

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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.