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In this paper, we believe that the mixed selectivity of neuron in the top layer encodes distributed information produced from other neurons to offer a significant computational advantage over recognition accuracy. Thus, this paper proposes a hierarchical network framework that the learning behaviors of features combined from hundreds of midlayers. First, a subnetwork neuron, which itself could be constructed by other nodes, is functional as a subspace features extractor. The top layer of a hierarchical network needs subspace features produced by the subnetwork neurons to get rid of factors that are not relevant, but at the same time, to recast the subspace features into a mapping space so that the hierarchical network can be processed to generate more reliable cognition. Second, this paper shows that with noniterative learning strategy, the proposed method has a wider and shallower structure, providing a significant role in generalization performance improvements. Hence, compared with other state-of-the-art methods, multiple channel features with the proposed method could provide a comparable or even better performance, which dramatically boosts the learning speed. Our experimental results show that our platform can provide a much better generalization performance than 55 other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As the most important property of neural networks (NNs), the universal approximation capability of NNs is widely used in many applications. However, this property is generally proven for continuous systems. Most industrial systems are hybrid systems (e.g., piecewise continuous), which is a significant limitation for real applications. Recently, many identification methods have been proposed for hybrid system approximation; however, these methods only operate in linear hybrid systems. In this paper, the progressive learning machine—a new learning algorithm based on multi-NNs—is proposed for general hybrid nonlinear/linear system approximation. This algorithm classifies hybrid systems into several continuous systems and can approximate any hybrid system with zero output error. The performance of the proposed learning method is demonstrated via numerical examples and with experimental data from real applications. [ABSTRACT FROM AUTHOR]

Fully connected representation learning (FCRL) is one of the widely used network structures in multimodel image classification frameworks. However, most FCRL-based structures, for instance, stacked autoencoder encode features and find the final cognition with separate building blocks, resulting in loosely connected feature representation. This article achieves a robust representation by considering a low-dimensional feature and the classifier model simultaneously. Thus, a new hierarchical subnetwork-based neural network (HSNN) is proposed in this article. The novelties of this framework are as follows: 1) it is an iterative learning process, instead of stacking separate blocks to obtain the discriminative encoding and the final classification results. In this sense, the optimal global features are generated; 2) it applies Moore–Penrose (MP) inverse-based batch-by-batch learning strategy to handle large-scale data sets, so that large data set, such as Place365 containing 1.8 million images, can be processed effectively. The experimental results on multiple domains with a varying number of training samples from ${\sim } 1 \,\,K$ to ${\sim }2~M$ show that the proposed feature reinforcement framework achieves better generalization performance compared with most state-of-the-art FCRL methods. [ABSTRACT FROM AUTHOR]

These days, detection of visual attention regions (VAR) such as moving objects have become an essential pre-processing stage in many computer vision applications. In this paper, we focus on the vital issue of separating moving objects a.k.a. Foreground (FG) in a scene, which has a near-static background (BG). We address the difficulty in setting an adaptive threshold in the multi-model Gaussian-based BG-FG separation through a novel FG enhancement strategy by assimilating color and illumination measures. We formulate the problem mathematically by using a histogram of a fused feature of color and illumination measures. The proposed method improves the BG-FG separation by introducing the following items: (i) A new distance measure to check if a pixel matches a Gaussian distribution. (ii) A new strategy to enhance the results of traditional background subtraction (BGS) with a fusion of color and illumination measures. (iii) A methodology to find appropriate threshold adaptively that separates BG and FG. (iv) A foreground validation process through probability estimation of multivariate Gaussian model distribution (MVGMD). We test the proposed algorithm on five different benchmark video sequences. The experimental results reveal that the proposed approach works well in challenging conditions, at the same time, it performs competitively against state-of-the-art Gaussian-based algorithms and few other non-Gaussian-based methods as well. [ABSTRACT FROM AUTHOR]

• In order to reestablish the random initialization input weights of ESN, a single layer ESN with a global reversible AE (GRAE) algorithm is proposed to ensure that the outputs of GRAE are remarkably correlated with the input data. • The reservoir layer with a reversible function is calculated by pulling the ESN output back and injecting it into the reservoir layer. Thus, the feature learning is enriched by additional information, which results in a better performance. • The GRAE which is based on ridge regression to solve a least squares problem is entirely different from existing back-propagation (BP) based AEs. So, the training speed of GRAE is many times faster than that of the BP based AEs. • In existing ESN autoencoder methods, all weights in the reservoir layer are generated randomly. The simple cycle connection is utilized in the reservoir layer of ESN to make sure the reservoir weights can be generated deterministically. An echo state network (z) can provide an efficient dynamic solution for predicting time series problems. However, in most cases, ESN models are applied for predictions rather than classifications. The applications of ESN in time series classification (TSC) problems have yet to be fully studied. Moreover, the conventional randomly generated ESN is unlikely to be optimal because of the randomly generated input and reservoir weights, which are not always guaranteed to be optimal. Randomly generating all layer weights is improper, because a purely random layer might destroy the useful features. To overcome this disadvantage, this study provides a new input weight establishment framework of ESN based on autoencoder (AE) theory for TSC tasks. A global reversible AE (GRAE) algorithm is proposed to reestablish the random initialization input weights of the ESN. In existing ESN-AEs, the output weights obtained in the encoding process are directly reused as the initial input weights. By contrast, in GRAE, the reservoir layer with a reversible activation function is calculated by pulling the decoding layer output back and injecting it into the reservoir layer. Thus, feature learning is enriched by additional information, which results in improved performance. The current weights of the encoding layer are iteratively replaced by the decoding layer to ensure that the outputs of the GRAE are remarkably correlated with the input data. Visualization analyses and experiments of the input weights on a massive set of UCR time series datasets indicate that the proposed GRAE method can considerably improve the original two-layer ESN-based classifiers and the proposed GRAE-ESN classifier yields better performance compared with traditional state-of-the-art TSC classifiers. Furthermore, the proposed method can provide comparable performance and considerably faster training speed compared with three deep learning classifiers. [ABSTRACT FROM AUTHOR]