Vector Quantization as Sparse Least Square Optimization
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Vector quantization aims to form new vectors/matrices with shared values close to the original. It could compress data with acceptable information loss, and could be of great usefulness in areas like Image Processing, Pattern Recognition and Machine Learning. In recent years, the importance of quantization has been soaring as it has been discovered huge potentials in deploying practical neural networks, which is among one of the most popular research topics. Conventional vector quantization methods usually suffer from their own flaws: hand-coding domain rules quantization could produce poor results when encountering complex data, and clustering-based algorithms have the problem of inexact solution and high time consumption. In this paper, we explored vector quantization problem from a new perspective of sparse least square optimization and designed multiple algorithms with their program implementations. Specifically, deriving from a sparse form of coefficient matrix, three types of sparse least squares, with l_0, l_1, and generalized l_1 + l_2 penalizations, are designed and implemented respectively. In addition, to produce quantization results with given amount of quantized values(instead of penalization coefficient λ), this paper proposed a cluster-based least square quantization method, which could also be regarded as an improvement of information preservation of conventional clustering algorithm. The algorithms were tested on various data and tasks and their computational properties were analyzed. The paper offers a new perspective to probe the area of vector quantization, while the algorithms proposed could provide more appropriate options for quantization tasks under different circumstances.
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