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Joint sparse representation (JSR) models have been widely applied into the field of hyperspectral image (HSI) classification. However, most of JSR-based models adopt the Frobenius norm to measure the reconstruction error, which ignores the structural information of the small patch. In this paper, we propose a nuclear-norm joint sparse representation (NuJSR) model for hyperspectral image classification...
We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship – an anchor point x is similar to a set of positive points Y , and dissimilar to a set of negative points Z, and a loss defined over these...
This paper proposes a regrouping particle swarm optimization-based neural network (RegPSONN) for rolling bearing fault diagnosis. The proposed method applied neural network for rolling bearing conditions classification, and regrouping particle swarm optimization (RegPSO) is utilized for network training, and ten time-domain feature parameters are selected to establish the input vector. To evaluate...
Discriminative dictionary learning has been widely used in many applications such as face retrieval / recognition and image classification, where the labels of the training data are utilized to improve the discriminative power of the learned dictionary. This paper deals with a new problem of learning a dictionary for associating pairs of images in applications such as face image retrieval. Compared...
Feature selection is an important task in machine learning, which aims to reduce the dataset dimensionality while at least maintaining the classification performance. Particle Swarm Optimisation (PSO) has been widely applied to feature selection because of its effectiveness and efficiency. However, since feature selection is a challenging task with a complex search space, PSO easily gets stuck at...
Stochastic gradient descent (SGD) is a commonly used technique in large-scale machine learning tasks, but its convergence is slow due to the inherent variance. In recent years, a popular method, Stochastic Variance Reduced Gradient (SVRG), addresses this shortcoming via computing the full gradient of the entire dataset in each epoch. However, conventional SVRG and its variants usually need to identify...
Stochastic gradient algorithms are the main focus of large-scale optimization problems and led to important successes in the recent advancement of the deep learning algorithms. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients. In this paper, we propose an adaptive learning rate algorithm, which utilizes stochastic...
This paper extends the random vector functional-link (RVFL) networks with single-hidden-layer to interval ones (IRVFLNs) with interval model parameters. The analytic solutions are derived for the interval network parameters using the well-known least square methods, which can overcome the problems such as local minimal, slow convergence. In order to evaluate the performance of IRVFLNs, we choose two...
Neural Networks have been successfully used in different fields of Information Security such that network intrusion detection and malware analysis because of ability to provide high level of abstraction for complex and incomplete data. Despite its successful application as off-line learning method, the on-line learning can be challenging when dealing with data streams. This paper presents an ongoing...
Optimization is important in neural networks to iteratively update weights for pattern classification. Existing optimization techniques suffer from suboptimal local minima and slow convergence rate. In this paper, stochastic diagonal Approximate Greatest Descent (SDAGD) algorithm is proposed to optimize neural network weights using multi-stage backpropagation manner. SDAGD is derived from the operation...
The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant of self-learning can be derived by applying block coordinate descent to two related but slightly different...
Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using “opposition-based learning” (OBL). Two types of the “opposites” have been defined in the literature, namely type-I and type-II. The former are linear in nature and applicable to the variable space, hence easy to calculate. On the other hand, type-II opposites capture...
This paper considers the long-term network resource allocation problem subject to queue stability. The dynamic problem is first reformulated as a static stochastic programming. To tackle the resultant static programming, we study its dual problem which contains finite number of variables in oppose to the primal problem that has infinite dimension. A novel online framework is developed by formulating...
In this paper, we focus on training a classifier from large-scale data with incompletely assigned labels. In other words, we treat samples with following properties: 1. assigned labels are definitely positive, 2. absent labels are not necessarily negative, and 3. samples are allowed to take more than one label. These properties are frequently found in various kinds of computer vision tasks, including...
Data volume has been increasing explosively in recent years and learning methods are vitally important to extract key information in such mass data. Traditional offline learning requires multiple traversals to the dataset, thus frequently suffering from lack of computational resources. Online learning can benefit in shrinking total time consumed by training model and lowering computational capacity...
Computational analysis of transcription factor binding site (TFBS) is one of the most challenging topics in bioinformatics. A set of TFBS sequences is a type of multiple sequence alignment (MSA). Thus, the hidden Markov model (HMM), as a powerful tool to model MSA, has been extensively applied in TFBS analysis. However, with the sizes of TFBS problems, training HMM in a deterministic way is computationally...
In this paper, we use a combination of support vector machine to improve the Standard SVM, which combine different kernel functions to improve the SVM' learning ability and generalization ability, thereby improving the performance of a combination SVM kernel function, and avoiding the assertiveness of the single prediction model. Combination forecasting model to make joint decisions on the results,...
Deep neural network (DNN) is trained according to a mini-batch optimization based on the stochastic gradient descent algorithm. Such a stochastic learning suffers from instability in parameter updating and may easily trap into local optimum. This study deals with the stability of stochastic learning by reducing the variance of gradients in optimization procedure. We upgrade the optimization from the...
In this paper we propose a novel algorithm, which is an improvement for one-stage dictionary learning (OS-DL) algorithm, by imposing a l2-norm constraint on the update of the atoms. Our contribution embarks from the OS-DL algorithm and incorporates the well-known convex optimization method, proximal point method, into this algorithm. Experimental results on recovering a known dictionary and sparsely...
Anomaly detection (AD) involves detecting abnormality from normality and has a wide spectrum of applications in reality. Kernel-based methods for AD have been proven robust with diverse data distributions and offering good generalization ability. Stochastic gradient descent (SGD) method has recently emerged as a promising framework to devise ultra-fast learning methods. In this paper, we conjoin the...
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