Entropy, Volume 22, Issue 1 (January 2020) – 127 articles
Cover Story (view full-size image):
Optimal lossy data compression minimizes the storage cost of a data set X while retaining a given amount of information as possible about something (Y) that you care about. For example, what aspects of an image X contain the most information about whether it depicts a cat or not?
We present a method for efficiently solving this problem and apply it to the CIFAR-10, MNIST, and Fashion-MNIST datasets, illustrating how it can be interpreted as an information-theoretically optimal image clustering algorithm. For merely a handful of clusters, compressing the image into a single integer specifying its cluster number retains almost all the information about its class label Y. View this paper.
We present a method for efficiently solving this problem and apply it to the CIFAR-10, MNIST, and Fashion-MNIST datasets, illustrating how it can be interpreted as an information-theoretically optimal image clustering algorithm. For merely a handful of clusters, compressing the image into a single integer specifying its cluster number retains almost all the information about its class label Y. View this paper.
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