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PubMed Journal Database | IEEE transactions on neural networks and learning systems RSS

02:16 EST 22nd November 2019 | BioPortfolio

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Showing PubMed Articles 1–25 of 589 from IEEE transactions on neural networks and learning systems

An Exponential-Type Anti-Noise Varying-Gain Network for Solving Disturbed Time-Varying Inversion Systems.

To solve the disturbed time-varying inversion problem, an exponential-type anti-noise varying-gain network (EAVGN) is proposed and analyzed. To do so, a vector-based error function is first defined. By using the varying-gain neural dynamic design method, an EAVGN model is then formulated. Furthermore, the differentiation error and the model-implementation error are considered into the model, and the perturbed EAVGN model is obtained. For better illustrations, comparisons between the EAVGN and the convention...

Adaptive Robust Low-Rank 2-D Reconstruction With Steerable Sparsity.

Existing image reconstruction methods frequently improve their robustness by using various nonsquared loss functions, which are still potentially sensitive to the outliers. More specifically, when certain samples in data sets encounter severe contamination, these methods cannot identify and filter out the ill ones, and thus lead to the functional degeneration of the associated models. To address this issue, we propose a general framework, named robust and sparse weight learning (RSWL), to compute the adapti...

MVStream: Multiview Data Stream Clustering.

This article studies a new problem of data stream clustering, namely, multiview data stream (MVStream) clustering. Although many data stream clustering algorithms have been developed, they are restricted to the single-view streaming data, and clustering MVStreams still remains largely unsolved. In addition to the many issues encountered by the conventional single-view data stream clustering, such as capturing cluster evolution and discovering clusters of arbitrary shapes under the limited computational reso...

Sketch Kernel Ridge Regression Using Circulant Matrix: Algorithm and Theory.

Kernel ridge regression (KRR) is a powerful method for nonparametric regression. The time and space complexity of computing the KRR estimate directly are O(n³) and O(n²), respectively, which are prohibitive for large-scale data sets, where n is the number of data. In this article, we propose a novel random sketch technique based on the circulant matrix that achieves savings in storage space and accelerates the solution of the KRR approximation. The circulant matrix has the following advantages: It can sav...

Region Stabilization of Switched Neural Networks With Multiple Modes and Multiple Equilibria: A Pole Assignment Method.

This article investigates region stabilization issue of switched neural networks (SNNs) with multiple modes (MMs) and multiple equilibria (ME) via a pole assignment method. In such an SNN, every neuron is observed with more than one mode and unstable equilibrium point. First, SNNs with MMs and ME are modeled in terms of switched systems with unstable subsystems and ME. Second, a necessary and sufficient condition and a sufficient condition are, respectively, proposed for arbitrary switching paths pole assig...

AlphaSeq: Sequence Discovery With Deep Reinforcement Learning.

Sequences play an important role in many applications and systems. Discovering sequences with desired properties has long been an interesting intellectual pursuit. This article puts forth a new paradigm, AlphaSeq, to discover desired sequences algorithmically using deep reinforcement learning (DRL) techniques. AlphaSeq treats the sequence discovery problem as an episodic symbol-filling game, in which a player fills symbols in the vacant positions of a sequence set sequentially during an episode of the game....

Secure Communication Based on Quantized Synchronization of Chaotic Neural Networks Under an Event-Triggered Strategy.

This article presents a secure communication scheme based on the quantized synchronization of master-slave neural networks under an event-triggered strategy. First, a dynamic event-triggered strategy is proposed based on a quantized output feedback, for which a quantized output feedback controller is formed. Second, theoretical criteria are derived to ensure the bounded synchronization of master-slave neural networks. With these criteria, an explicit upper bound is given for the synchronization error. Suffi...

Distributed Selection of Continuous Features in Multilabel Classification Using Mutual Information.

Multilabel learning is a challenging task demanding scalable methods for large-scale data. Feature selection has shown to improve multilabel accuracy while defying the curse of dimensionality of high-dimensional scattered data. However, the increasing complexity of multilabel feature selection, especially on continuous features, requires new approaches to manage data effectively and efficiently in distributed computing environments. This article proposes a distributed model for mutual information (MI) adapt...

Exact Passive-Aggressive Algorithms for Ordinal Regression Using Interval Labels.

In this article, we propose exact passive-aggressive (PA) online algorithms for ordinal regression. The proposed algorithms can be used even when we have interval labels instead of actual labels for example. The proposed algorithms solve a convex optimization problem at every trial. We find an exact solution to those optimization problems to determine the updated parameters. We propose a support class algorithm (SCA) that finds the active constraints using the Karush-Kuhn-Tucker (KKT) conditions of the opti...

Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance Targeting Neuromorphic Processors.

The Lobula giant movement detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect's escape responses. Understanding the neural principles and network structure that leads to these fast and robust responses can facilitate the design of efficient obstacle avoidance strategies for robotic applications. Here, we present a neuromorphic spiking neural network model of the LGMD driven by the output of a neuromorphic dynamic vision sensor (DVS), which incorporates ...

Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation.

Semi-supervised non-negative matrix factorization (NMF) exploits the strengths of NMF in effectively learning local information contained in data and is also able to achieve effective learning when only a small fraction of data is labeled. NMF is particularly useful for dimensionality reduction of high-dimensional data. However, the mapping between the low-dimensional representation, learned by semi-supervised NMF, and the original high-dimensional data contains complex hierarchical and structural informati...

Supervised Dimensionality Reduction Methods via Recursive Regression.

In this article, the recursive problems of both orthogonal linear discriminant analysis (OLDA) and orthogonal least squares regression (OLSR) are investigated. Different from other works, the associated recursive problems are addressed via a novel recursive regression method, which achieves the dimensionality reduction in the orthogonal complement space heuristically. As for the OLDA, an efficient method is developed to obtain the associated optimal subspace, which is closely related to the orthonormal basi...

On the Working Principle of the Hopfield Neural Networks and Its Equivalence to the GADIA in Optimization.

Hopfield neural networks (HNNs) are one of the most well-known and widely used kinds of neural networks in optimization. In this article, the author focuses on building a deeper understanding of the working principle of the HNN during an optimization process. Our investigations yield several novel results giving some important insights into the working principle of both continuous and discrete HNNs. This article shows that what the traditional HNN actually does as energy function decreases is to divide the ...

Automatically Design Convolutional Neural Networks by Optimization With Submodularity and Supermodularity.

The architecture of convolutional neural networks (CNNs) is a key factor of influencing their performance. Although deep CNNs perform well in many difficult problems, how to intelligently design the architecture is still a challenging problem. Focusing on two practical architectural design problems: to maximize the accuracy with a given forward running time and to minimize the forward running time with a given accuracy requirement, we innovatively utilize prior knowledge to convert architecture optimization...

Cross-Batch Reference Learning for Deep Retrieval.

Learning effective representations that exhibit semantic content is crucial to image retrieval applications. Recent advances in deep learning have made significant improvements in performance on a number of visual recognition tasks. Studies have also revealed that visual features extracted from a deep network learned on a large-scale image data set (e.g., ImageNet) for classification are generic and perform well on new recognition tasks in different domains. Nevertheless, when applied to image retrieval, su...

Memory Augmented Deep Recurrent Neural Network for Video Question Answering.

Video question answering (VideoQA) is a very important but challenging multimedia task, which automatically analyzes questions and videos and generates accurate answers. However, research on VideoQA is still in its infancy. In this article, we propose a novel memory augmented deep recurrent neural network (MA-DRNN) model for VideoQA, which features a new method for encoding videos and questions, and memory augmentation using the emerging differentiable neural computer (DNC). Specifically, we encode textual ...

Unsupervised Anomaly Detection With LSTM Neural Networks.

We investigate anomaly detection in an unsupervised framework and introduce long short-term memory (LSTM) neural network-based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM-based structure and obtain fixed-length sequences. We then find a decision function for our anomaly detectors based on the one-class support vector machines (OC-SVMs) and support vector data description (SVDD) algorithms. As the first time in the literature, we jointly tra...

Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search.

Hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. Recent supervised hashing research has shown that deep learning-based methods can significantly outperform nondeep methods. Most existing supervised deep hashing methods exploit supervisory signals to generate similar and dissimilar image pairs for training. However, natural images can have large intraclass and small interclass variations, which may degrade the accuracy of hash codes. T...

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels.

Random Fourier features (RFFs) have been successfully employed to kernel approximation in large-scale situations. The rationale behind RFF relies on Bochner's theorem, but the condition is too strict and excludes many widely used kernels, e.g., dot-product kernels (violates the shift-invariant condition) and indefinite kernels [violates the positive definite (PD) condition]. In this article, we present a unified RFF framework for indefinite kernel approximation in the reproducing kernel Kreĭn spaces (RKKSs...

Weighted Broad Learning System and Its Application in Nonlinear Industrial Process Modeling.

Broad learning system (BLS) is a novel neural network with effective and efficient learning ability. BLS has attracted increasing attention from many scholars owing to its excellent performance. This article proposes a weighted BLS (WBLS) based on BLS to tackle the noise and outliers in an industrial process. WBLS provides a unified framework for easily using different methods of calculating the weighted penalty factor. Using the weighted penalty factor to constrain the contribution of each sample to modeli...

Siamese Dilated Inception Hashing With Intra-Group Correlation Enhancement for Image Retrieval.

For large-scale image retrieval, hashing has been extensively explored in approximate nearest neighbor search methods due to its low storage and high computational efficiency. With the development of deep learning, deep hashing methods have made great progress in image retrieval. Most existing deep hashing methods cannot fully consider the intra-group correlation of hash codes, which leads to the correlation decrease problem of similar hash codes and ultimately affects the retrieval results. In this article...

Does Tail Label Help for Large-Scale Multi-Label Learning?

Large-scale multi-label learning (LMLL) annotates relevant labels for unseen data from a huge number of candidate labels. It is perceived that labels exhibit a long tail distribution in which a significant number of labels are tail labels. Most previous studies consider that the performance would benefit from incorporating tail labels. Nonetheless, it is not quantified how tail labels impact the performance. In this article, we disclose that whatever labels are randomly missing or misclassified, the impact ...

Unsupervised Domain Adaptation With Adversarial Residual Transform Networks.

Domain adaptation (DA) is widely used in learning problems lacking labels. Recent studies show that deep adversarial DA models can make markable improvements in performance, which include symmetric and asymmetric architectures. However, the former has poor generalization ability, whereas the latter is very hard to train. In this article, we propose a novel adversarial DA method named adversarial residual transform networks (ARTNs) to improve the generalization ability, which directly transforms the source f...

A Deep One-Class Neural Network for Anomalous Event Detection in Complex Scenes.

How to build a generic deep one-class (DeepOC) model to solve one-class classification problems for anomaly detection, such as anomalous event detection in complex scenes? The characteristics of existing one-class labels lead to a dilemma: it is hard to directly use a multiple classifier based on deep neural networks to solve one-class classification problems. Therefore, in this article, we propose a novel DeepOC neural network, termed as DeepOC, which can simultaneously learn compact feature representation...

Neural Probabilistic Graphical Model for Face Sketch Synthesis.

Neural network learning for face sketch synthesis from photos has attracted substantial attention due to its favorable synthesis performance. However, most existing deep-learning-based face sketch synthesis models stacked only by multiple convolutional layers without structured regression often lose the common facial structures, limiting their flexibility in a wide range of practical applications, including intelligent security and digital entertainment. In this article, we introduce a neural network to a p...


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