ECA-BLS: An Efficient Complex-Augmented Broad Learning System

• URL: http://arxiv.org/abs/2608.29763v1
• Date: Sun, 30 Aug 2026 12:50:13 GMT
• Title: ECA-BLS: An Efficient Complex-Augmented Broad Learning System
• Abstract summary: Broad Learning System (BLS) is an efficient alternative to deep architectures due to its fast training, analytical learning, and strong generalization under limited data.<n>No prior BLS model fully exploits the complete second-order statistics that naturally emerge when data are embedded in the complex domain.<n>This paper introduces the first complex augmented Broad Learning System (CA-BLS), which transforms real-valued inputs into phase-encoded complex representations.
• Score: 8.19984844136462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• SMI: Efficient Self-Supervised Learning via Mutual-Information-Inspired Dependency Optimization [8.864968998286772]
  Self-supervised learning (SSL) has achieved remarkable representation learning performance.<n>Many existing methods rely on large batch sizes, memory banks, momentum encoders, or global synchronization mechanisms.<n>We propose Semantic Mutual Information (SMI), a lightweight self-supervised objective derived from a mutual-information-inspired dependency formulation.
  arXiv  Detail & Related papers  (2026-06-06T20:50:04Z)
• Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases [66.24230877709756]
  relational deep learning (RDL) is proposed to structure the relational databases (RDB) as a heterogeneous entity graph.<n>Existing RDL methods neglect the imbalance problem of relational data in RDBs and risk under-representing the minority entities.<n>In this work, we investigate for the first time, class imbalance problem in RDB entity classification and design the relation-centric minority synthetic over-sampling GNN.
  arXiv  Detail & Related papers  (2026-03-09T03:18:26Z)
• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration [56.074760766965085]
  PRISM achieves a dynamics-aware framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge.<n>Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.
  arXiv  Detail & Related papers  (2026-01-12T05:43:20Z)
• Opinion Mining and Analysis Using Hybrid Deep Neural Networks [0.0]
  We introduce a hybrid deep neural network model that combines a bidirectional gated recurrent unit (BGRU) and long short-term memory (LSTM) layers to improve sentiment analysis.<n>Our model exhibited a noteworthy enhancement in recall for negative sentiments, escalating from 86% (unbalanced dataset) to 96% (balanced dataset)
  arXiv  Detail & Related papers  (2025-11-16T19:04:44Z)
• Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models [53.339700196282905]
  A key challenge in applying reinforcement learning to large language models (dLLMs) is the intractability of their likelihood functions.<n>We propose a memory-efficient RL algorithm that maximizes a specially constructed lower bound of the ELBO-based objective.<n> Experiments show that BGPO significantly outperforms previous RL algorithms for dLLMs in math problem solving, code generation, and planning tasks.
  arXiv  Detail & Related papers  (2025-10-13T17:47:50Z)
• Explicit modelling of subject dependency in BCI decoding [12.17288254938554]
  Brain-Computer Interfaces (BCIs) suffer from high inter-subject variability and limited labeled data.<n>We present an end-to-end approach that explicitly models the subject dependency using lightweight convolutional neural networks (CNNs) conditioned on the subject's identity.
  arXiv  Detail & Related papers  (2025-09-27T10:51:42Z)
• NDCG-Consistent Softmax Approximation with Accelerated Convergence [67.10365329542365]
  We propose novel loss formulations that align directly with ranking metrics.<n>We integrate the proposed RG losses with the highly efficient Alternating Least Squares (ALS) optimization method.<n> Empirical evaluations on real-world datasets demonstrate that our approach achieves comparable or superior ranking performance.
  arXiv  Detail & Related papers  (2025-06-11T06:59:17Z)
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework [70.83511997272457]
  We propose a semi-federated learning (SemiFL) paradigm to leverage both the base station (BS) and devices for a hybrid implementation of centralized learning (CL) and FL. We propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers.
  arXiv  Detail & Related papers  (2023-10-04T03:32:39Z)
• Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and Outliers [0.0]
  We propose fuzzy broad learning system (F-BLS) and intuitionistic fuzzy broad learning system (IF-BLS) models. We implement the proposed F-BLS and IF-BLS models to diagnose Alzheimer's disease (AD)
  arXiv  Detail & Related papers  (2023-07-15T21:40:36Z)
• DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths [45.947140164621096]
  We propose a new approach based on differential inclusions of inverse scale spaces. We show that DessiLBI unveils "winning tickets" in early epochs.
  arXiv  Detail & Related papers  (2020-07-04T04:40:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.