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ECA-BLS: An Efficient Complex-Augmented Broad Learning System — arXiv:2608.29763

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29763
License (as archived): CC0-1.0
Archived: 2026-10-17T08:36:54.410Z
SHA-256 of the archived text: fdc8d4b2377c82fa34c9dee7369ce13043b9910527306977d46e1b3e9faef515
Stable URL of this page: https://metraipolis.com/documento/dc_fc6ef5cd212e

How to cite

ECA-BLS: An Efficient Complex-Augmented Broad Learning System — arXiv:2608.29763 — arXiv (en), https://arxiv.org/abs/2608.29763 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fc6ef5cd212e (Metraipolis archive dc_fc6ef5cd212e, sha256 fdc8d4b2377c82fa34c9dee7369ce13043b9910527306977d46e1b3e9faef515)

BibTeX

@misc{dcfc6ef5cd212e,
  title = {ECA-BLS: An Efficient Complex-Augmented Broad Learning System — arXiv:2608.29763},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29763},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fc6ef5cd212e; sha256 fdc8d4b2377c82fa34c9dee7369ce13043b9910527306977d46e1b3e9faef515},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29763. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Broad Learning System (BLS) is an efficient alternative to deep architectures due to its fast training, analytical learning, and strong generalization under limited data. However, existing BLS variants are confined to real-valued representations, restricting their ability to capture nonlinear interactions and second-order statistical dependencies inherent in real-world data. Notably, no prior BLS model fully exploits the complete second-order statistics that naturally emerge when data are embedded in the complex domain. To address this limitation, this paper introduces the first complex augmented Broad Learning System (CA-BLS), which transforms real-valued inputs into phase-encoded complex representations and adopts widely linear modeling to jointly leverage covariance and pseudo-covariance information via complex conjugate augmentation. This enables effective modeling of latent nonlinearities, coherence structures, and second-order dependencies inaccessible to conventional BLS formulations. To mitigate the additional computational cost of complex augmentation, an Efficient Complex Augmented BLS (ECA-BLS) is further developed, reformulating CA-BLS entirely in the real domain while preserving its exact decision function, achieving up to 75\% fewer multiplications and over 60\% fewer additions. A rigorous theoretical analysis proves the mathematical equivalence between CA-BLS and ECA-BLS, ensuring zero theoretical loss. Extensive experiments on 26 benchmark datasets from the UCI and KEEL repositories demonstrate that ECA-BLS consistently outperforms classical BLS and recent state-of-the-art randomized neural networks in accuracy, average rank, and statistical significance, establishing augmented second-order modeling as a critical and previously missing dimension of BLS research.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.29763 — 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 2026-10-17T08:13:26.765Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).