Algorithms and computational-architectures for change detections in large-scale data

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In this research, we study the change detection problem, i.e., the problem of detecting systematic changes in data generating processes only from observed time-series data. Especially, we develop efficient algorithms that explicitly take the scale of data into consideration. To this end, we developed an algorithm based on recently proposed density-ratio estimation techniques. And, we derived an online implementation of this algorithm, which make the change detection algorithm much more efficient. Since change detection is a technology well suited for applications including fault diagnosis in engineering systems, our results would be significant also as a fundamental technology for such applications.