Understand
We consider the task of lossy compression of high-dimensional vectors through quantization.
- We propose the approach that learns quantization parameters by minimizing the distortion of scalar products and squared distances between pairs of points.
- This is in contrast to previous works that obtain these parameters through the minimization of the reconstruction error of individual points.
- The proposed approach proceeds by finding a linear transformation of the data that effectively reduces the minimization of the pairwise distortions to the minimization of individual reconstruction errors.
Built on
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Approximate nearest neighbor search by residual vector quantization
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Then
Additive quantization for extreme vector compression
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T. Zhang, C. Du, and J. Wang · 2014
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