Almost-lossless compression of a low-rank random tensor
arXiv:2210.04041v2h-index: 5
Originality Synthesis-oriented
AI Analysis
This work addresses compression efficiency for data structures like tensors, but appears incremental as it builds on existing low-rank decomposition methods.
The paper tackled the problem of compressing low-rank random tensors with finite alphabets, establishing an asymptotic limit for almost-lossless compression.
In this work, we establish an asymptotic limit of almost-lossless compression of a random, finite alphabet tensor which admits a low-rank canonical polyadic decomposition.