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Ordered data compression system and methods using principle component analysis
Ordered data compression system and methods using principle component analysis
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机译:使用主成分分析的有序数据压缩系统和方法
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摘要
Methods and systems are provided for encoding, transmission and decoding of vectorized input data, for example, video or audio data. A convex invariance learning framework is established for processing input data or a given data type. Each input vector is associated with a variable transformation matrix that acts on the vector to invariantly permute the vector elements. Joint invariance and model learning is performed on a training set of invariantly transformed vectors over a constrained space of transformation matrices using maximum likelihood analysis. The maximum likelihood analysis reduces the data volume to a linear subspace volume in which the training data can be modeled by a reduced number of variables. Principal component analysis is used to identify a set of N eigenvectors that span the linear subspace. The set of N eigenvectors is used a basis set to encode input data and to decode compressed data.
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