Learning-Oriented Data Compression with Applications

点击翻译按钮获取中文摘要

With the ever-growing number of mobile devices, as well as the widespread deployment of surveillance systems and sensor networks, an unprecedented amount of data is being generated and collected. Data compression techniques are playing an increasingly important role in meeting the significant challenges in storing, transmitting, processing, and analyzing such data. The proposed research aims to develop a theory of learning-oriented data compression, investigate the relevant algorithm design, and explore the practical applications of this new data compression paradigm. It represents a stepping stone in the long-term goal of developing a full-fledged compression-aware learning theory.******The proposed research consists of three thrusts: centralized compression, distributed compression, and generalized distributed compression.******In a learning-oriented centralized compression system, a source is encoded into a bit string, and based on that string, the decoder produces a probabilistic description of the relevant features of the target data satisfying the prescribed constraints. A unified approach will be developed based on a novel optimization technique for estimating the fundamental compression limits and generating the optimal feature representations. Compact descriptors for video analysis will be constructed based on this approach. This line of work also has the potential of shedding light on the design and compression of deep neural networks. ******Learning-oriented distributed compression generalizes its centralized counterpart by separately encoding disjoint source components. For this problem, traditional approaches based on information geometry are only effective in the low-rate limit. Significant analytical difficulties arise in the high-rate region due to the non-convex nature of the problem. Advanced analytical techniques will be introduced to tackle these difficulties. Among many other applications, the resulting theory will be used to guide the joint design of the signaling and quantizing subsystems in cloud radio access networks.******Learning-oriented generalized distributed data compression is a further extension of distributed data compression that allows arbitrary connections between the source components and the encoders. The theory of learning-oriented generalized distributed data compression, once completed, can be leveraged to design an enhanced predictive coding architecture that is able to achieve the performance limit of noncausal lossy compression.******The proposed research will greatly enrich the theory of data compression and broaden the scope of its applications. The students involved in this research program will receive a balanced and comprehensive training in theory building, algorithm design, and system implementation. The breadth of the insight that they will acquire will enable them to become leaders of this emerging research field.