# Lossy Compression, Classification, and Regression

## Abstract

The traditional goal of data compression is to speed transmission or to minimize storage requirements of a signal while preserving the best possible quality of reproduction. This is usually formalized by trying to minimize the average distortion between the input and output in the sense of mean squared error (MSE) or a similar measure, subject to a constraint on the average bit rate. Measures of distortion are intended to ensure that low average distortion means the reconstructed signal will “look like” or “sound like” the original uncompressed signal. We will refer to the MSE throughout, but we mean it in the general sense of any applicable measure of distortion. The constrained optimization problem leads to theoretical analysis, providing optimality properties and performance bounds, and design algorithms for a variety of code structures. Numerous methods can be used to design a MSE minimizing vector quantization (VQ), including clustering algorithms such as the Lloyd or k-means algorithm and their tree-structured extensions [1]; algorithms based on neural net ideas such as competitive learning, backpropagation, and self organizing feature maps; and algorithms for deterministic annealing.

## Keywords

Mean Square Error Minimum Mean Square Error Near Neighbor Vector Quantization Lossy Compression## Preview

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