Quantum data compression by principal component analysis

22 Nov 2018 Yu Chao-Hua Gao Fei Lin Song Wang Jingbo

Data compression can be achieved by reducing the dimensionality of high-dimensional but approximately low-rank datasets, which may in fact be described by the variation of a much smaller number of parameters. It often serves as a preprocessing step to surmount the curse of dimensionality and to gain efficiency, and thus it plays an important role in machine learning and data mining... (read more)

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