Metraipolis › archive › dc_87f4493ab101

SETAREH Transform: A Sorting-Assisted Haar-Wavelet Sparsification Method for Approximate Inner-Product Computation — arXiv:2610.23338

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.23338
License (as archived): CC0-1.0
Archived: 2026-10-19T08:48:44.468Z
SHA-256 of the archived text: 6e26690df1102700bf76029ecdc7c05c9c7d0ecd7ecb72ccd081b4829bd7f84c
Stable URL of this page: https://metraipolis.com/documento/dc_87f4493ab101

How to cite

SETAREH Transform: A Sorting-Assisted Haar-Wavelet Sparsification Method for Approximate Inner-Product Computation — arXiv:2610.23338 — arXiv (en), https://arxiv.org/abs/2610.23338 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_87f4493ab101 (Metraipolis archive dc_87f4493ab101, sha256 6e26690df1102700bf76029ecdc7c05c9c7d0ecd7ecb72ccd081b4829bd7f84c)

BibTeX

@misc{dc87f4493ab101,
  title = {SETAREH Transform: A Sorting-Assisted Haar-Wavelet Sparsification Method for Approximate Inner-Product Computation — arXiv:2610.23338},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.23338},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_87f4493ab101; sha256 6e26690df1102700bf76029ecdc7c05c9c7d0ecd7ecb72ccd081b4829bd7f84c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.23338. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Modern deep learning systems increasingly rely on high-dimensional vector operations, in which large input and parameter vectors are repeatedly stored, transferred, and processed. Although substantial research has focused on reducing neural-network model size through pruning, quantization, knowledge distillation, and efficient architectures, the storage, memory-traffic, and communication costs associated with repeatedly presented high-dimensional input vectors remain important practical considerations, particularly in distributed and edge-computing environments. This paper proposes the SETAREH (Sorting-assisted Energy-concentrating Transform for Approximate Representation using Haar wavelets) transform, a Haar-wavelet-based sparsification approach for approximating the inner product between a fixed high-dimensional coefficient vector and successive input vectors. By applying the same sequence of permutations and Haar transforms to both vectors, SETAREH preserves the exact inner product before truncation. It then retains only a subset of the transformed coefficient-input pairs, reducing their representation cost while introducing a controllable approximation error. To evaluate SETAREH, we consider two complementary experimental settings: a synthetic Gaussian-vector baseline and a real apparent-age estimation task using a convolutional neural network on the APPA-REAL dataset. The results show that SETAREH can approximate the evaluated inner products using substantially fewer transformed coefficient-input pairs than the original vector dimension. The findings support its use as a potential structured data-representation method for repeated inner products with fixed weights, while the practical benefit depends on approximation accuracy and the cost of storing or transmitting permutation metadata.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.23338 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T08:42:40.274Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).