  This paper presents a fast decorrelated neuro-ensemble with heterogeneous
features for large-scale data analytics, where stochastic configuration
networks (SCNs) are employed as base learner models and the well-known negative
correlation learning (NCL) strategy is adopted to evaluate the output weights.
By feeding a large number of samples into the SCN base models, we obtain a huge
sized linear equation system which is difficult to be solved by means of
computing a pseudo-inverse used in the least squares method. Based on the group
of heterogeneous features, the block Jacobi and Gauss-Seidel methods are
employed to iteratively evaluate the output weights, and a convergence analysis
is given with a demonstration on the uniqueness of these iterative solutions.
Experiments with comparisons on two large-scale datasets are carried out, and
the system robustness with respect to the regularizing factor used in NCL is
given. Results indicate that the proposed ensemble learning techniques have
good potential for resolving large-scale data modelling problems.
