Semantic Compression with Information Lattice Learning
arxiv(2024)
摘要
Data-driven artificial intelligence (AI) techniques are becoming prominent
for learning in support of data compression, but are focused on standard
problems such as text compression. To instead address the emerging problem of
semantic compression, we argue that the lattice theory of information is
particularly expressive and mathematically precise in capturing notions of
abstraction as a form of lossy semantic compression. As such, we demonstrate
that a novel AI technique called information lattice learning, originally
developed for knowledge discovery and creativity, is powerful for learning to
compress in a semantically-meaningful way. The lattice structure further
implies the optimality of group codes and the successive refinement property
for progressive transmission.
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