In order to improve the effect of laser image classification, a laser image classification and recognition method based on large data analysis technology is designed according to the large-scale characteristics of laser image. Firstly, the roughness, orientation and contrast texture features of laser image are determined to form the texture feature data field of laser image. Then, Spark parallel support vector machine algorithm is introduced to establish image classifier. Laser image classification and recognition are realized according to image classifier. Finally, the simulation comparison of laser image classification and recognition is carried out. Test. The test results show that the efficiency and accuracy of this method are superior to each other, and it can fully meet the practical application requirements of large-scale laser image classification and recognition.

Principal component analysis network (PCANet), as an unsupervised shallow network, demonstrates noticeable effectiveness on datasets of various volumes. It carries a two-layer convolution with PCA as filter learning method, followed by a block-wise histogram post-processing stage. Following the structure of PCANet, extreme learning machine auto-encoder (ELM-AE) variants are employed to replace the PCA's role, which come from extreme learning machine network (ELMNet) and hierarchical ELMNet. ELMNet emphasizes the importance of orthogonal projection while overlooking non-linearity. The latter introduces complex pre-processing to overcome drawback of non-linear ELM-AE. In this paper, we analyze intrinsic characteristics of ELM-AE variants and accordingly propose a regularized ELM-AE, which combines non-linearity learning capability and approximately orthogonal projection. Experiments on image classification show the effectiveness compared to supervised convolutional neural networks and related shallow networks on unsupervised feature learning.

Recently, learning data representations have been investigated to reduce the dependences of human intervention and improve the performance of machine fault diagnosis. However, most of the representation learning methods are computationally intensive due to complex training procedures. Extreme learning machine is well-known for its fast training speed and strong generalization ability. It also has been applied to learn data representations for clustering and classification tasks. In this paper, a local discriminant preserving extreme learning machine autoencoder (LDELM-AE) is proposed to learn data representations with the local geometry and local discriminant exploited from the input data. Specifically, LDELM-AE utilizes two graphs to enhance the within-class compactness and between-class separability, respectively. Furthermore, the hierarchical representations can be obtained by stacking several LDELM-AEs. On several benchmark datasets, the proposed method demonstrates better classification accuracies than the state-of-the-art methods. Moreover, the proposed method has been used to diagnostic the rotary machine faults and achieves the diagnostic accuracy of 99.96%, which proves the proposed method is an efficient tool to diagnose machine faults.

To address the inability of current hyperspectral image processing methods to effectively classify hyperspectral images and to improve classification accuracy, a hyperspectral image classification method based on deep learning theory is proposed. First, the research progress of hyperspectral image classification is analyzed to identify the reasons affecting classification accuracy. Then, feature extraction and selection are performed to establish a feature vector set for hyperspectral image classification. Finally, deep learning theory is introduced to learn the classification features of hyperspectral images, and a hyperspectral image classifier is constructed. Simulation experiments were conducted using commonly used hyperspectral image datasets. The results show that the classification accuracy of the proposed method exceeds 90%, significantly reducing classification errors. The classification results are highly stable and outperform current methods for hyperspectral image classification.

Extreme Learning Machine (ELM) is a powerful and favorable classifier used in various applications due to its fast speed and good generalization capability. However, when dealing with complex visual tasks, the shallow architecture of ELM makes it infeasible to have good performance when raw image data are directly fed in as input. Therefore, several works tried to make use of deep neural networks (DNNs) to extract features before ELM classification. On the other hand, when the depth of DNN is too deep, the ELM classifier may suffer from overfitting problem. To solve this issue, a novel deep and wide feature based Extreme Learning Machine (DW-ELM) has been proposed in this research work. We show that the overfitting problem can be largely remedied by employing a "widened" convolutional neural network (CNN) for feature extraction, in the sense that the number of feature maps for each convolutional layer is increased by factor of k compared to a reference model, i.e. deep residual networks (ResNets). While the wide design of residual networks has been shown to benefit image classification in terms of accuracy and efficiency, its application for feature extraction is not fully investigated. We provide an extensive experimental study in this work, showing that when combined with ELM that serves as a classifier, using wide ResNets (WRNs) for feature extraction can produce a performance leap on all benchmark datasets compared to a plain end-to-end trained network over a wide range of selections regardless of architecture choices and ELM designs, while normal ResNets as feature extractors do not provide a performance gain. The gap is even larger when fewer training iterations are employed. This indicates that a good feature extractor for ELM must be wide and deep. Experiments conducted on five benchmark datasets (CIFAR-100, CIFAR-10, STL-10, Flower-102 and Fashion-MNIST) have shown significant accuracy enhancement as well as training stability of the proposed DW-ELM. Ablation studies also demonstrate that the ELM classifier is an important component for DW-ELM which enables superior performance compared with SVM based image classification approaches. (c) 2020 Elsevier B.V. All rights reserved.

For data with various complicated distribution in the original feature space, it is difficult to find the clusters of the data. Extreme learning machine (ELM) is famous for its universal approximation capability and the hidden space created by random nonlinear feature mapping. Existing ELM based clustering methods address this by constructing an embedding space, in which the cluster are easily revealed. A commonality of them is the final results have to be subsequently derived by k-means clustering. In this paper, we propose an unsupervised feature selection based extreme learning machine (UFSELM) for clustering, which integrates ELM with L2,1 norm regularization to remove the worthless hidden neurons and clusters the data directly without building an embedding. Specifically, the proposed method conducts feature selection by minimizing the L2,1 norm of output weights, and the clustering results is computed by eigendecomposition. By solving the formulated optimization problem in an iterative fashion, we improved the accuracy of clustering. We conducted experiments on several public datasets to demonstrate the effectiveness of the proposed method and further analyzed the properties of the proposed method.

Recently, deep learning-based representation learning methods have attracted increasing attention in machine fault diagnosis. However, few existing methods consider the geometry of data samples. In this paper, we propose a novel method to obtain representations that preserve the geometry of input data. More specifically, we formulate two cost functions to preserve the local and global geometries of input data, respectively and another cost function to reconstruct the input data. Furthermore, to simplify the training process, we formulate a discrimination cost function based on the label information. By jointly optimizing all cost functions, the method can efficiently learn discriminative representations with the local and global geometry of input data preserved. Furthermore, the proposed method can obtain hierarchical representations without any additional tuning step. On two benchmark datasets, the proposed method demonstrates better fault classification performance and shorter training and test time. Therefore, it is an efficient tool to provide accurate information about machine conditions for making maintenance decision and saving costs.

In this paper an effective graph learning method is proposed for clustering based on adaptive graph regularizations. Many graph learning methods focus on optimizing a global constraint on sparsity, low-rankness or weighted pair-wise distances, but they often fail to consider local connectivities. We demonstrate the importance of locality by generalizing the Locality-constrained Linear Coding (LLC) for unsupervised learning. Each data sample is expressed as a representation of its nearest neighbors, which naturally leads to a combination of distance regularized features and a Locally Linear Embedding (LLE) decomposition. The representation enforces a locally sparse connection on the data graph that exhibits high discrimination power and is easy to optimize. To improve the learned graph structure and incorporate cluster information, a rank constraint is further imposed on the Laplacian matrix of the data graph so that the connected components match the class number. The obtained representations are smoothed via manifold regularizations on a predefined graph which serves as a prior for graph learning. Finally, we utilize unsupervised Extreme Learning Machine (US-ELM) to learn a flexible and discriminative data embedding. Extensive evaluations show that the proposed algorithm outperforms graph learning counterpart methods on a wide range of benchmark datasets.

Recently, preserving geometry information of data while learning representations have attracted increasing attention in intelligent machine fault diagnosis. Existing geometry preserving methods require to predefine the similarities between data points in the original data space. The predefined affinity matrix, which is also known as the similarity matrix, is then used to preserve geometry information during the process of representations learning. Hence, the data representations are learned under the assumption of a fixed and known prior knowledge, i.e., similarities between data points. However, the assumed prior knowledge is difficult to precisely determine the real relationships between data points, especially in high dimensional space. Also, using two separated steps to learn affinity matrix and data representations may not be optimal and universal for data classification. In this paper, based on the extreme learning machine autoencoder (ELM-AE), we propose to learn the data representations and the affinity matrix simultaneously. The affinity matrix is treated as a variable and unified in the objective function of ELM-AE. Instead of predefining and fixing the affinity matrix, the proposed method adjusts the similarities by taking into account its capability of capturing the geometry information in both original data space and non-linearly mapped representation space. Meanwhile, the geometry information of original data can be preserved in the embedded representations with the help of the affinity matrix. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed method, and the empirical study also shows it is an efficient tool on machine fault diagnosis.

基因表达数据的研究是生物医学上的一个重要课题.基于其高维度、小样本的特点,特征选择已经成为数据预处理阶段的关键步骤.单一特征选择方法得到的特征子集可能会有偏差,本研究在特征选择上引入集成学习的思想,构建集成特征选择模型,并将此模型应用到3个不同的基因表达数据集上.为了评价特征子集的分类预测性能,使用支持向量机作为分类器进行测试.实验结果表明:相对于单一的特征选择方法,集成特征选择能够有效提高分类模型准确度.

Dictionary learning is a widely adopted approach for image classification. Existing methods focus either on finding a dictionary that produces discriminative sparse representation, or on enforcing priors that best describe the dataset distribution. In many cases, the dataset size is often small with large intra-class variability and nondiscriminative feature space. In this work we propose a simple and effective framework called ELM-DDL to address these issues. Specifically, we represent input features with Extreme Learning Machine (ELM) with orthogonal output projection, which enables diverse representation on nonlinear hidden space and task specific feature learning on output space. The embeddings are further regularized via a maximum margin criterion (MMC) to maximize the inter-class variance and minimize intra-class variance. For dictionary learning, we design a novel weighted class specific ℓ1,2 norm to regularize the sparse coding vectors, which promotes uniformity of the sparse patterns of samples belonging to the same class and suppresses support overlaps of different classes. We show that such regularization is robust, discriminative and easy to optimize. The proposed method is combined with a sparse representation classifier (SRC) to evaluate on benchmark datasets. Results show that our approach achieves state-of-the-art performance compared to other dictionary learning methods.

Monitoring the presence of occupants in a room in a timely manner is a fundamental step for effective building management. Environmental sensor networks have the advantages of high cost-efficiency and non-intrusiveness on privacy and are very suitable for room occupancy detection. Nonlinear discriminative models, e.g., support vector machine and neural networks, have shown good detection performance due to their ability to model complex relationship. However, they tend to produce unstable detection with frequent fluctuations over time, because they regard training data as independent and ignore the prior knowledge of the room occupancy, i.e., not changing very frequently. To improve the stability of the detection, we propose a two-stage structured learning approach with Extreme Learning Machine (ELM) as the local classifier. In the first stage, ELM is used as a fast nonlinear classifier to obtain preliminary detection results. In the second stage, we form data sequences consisting of the current and previous data points. The preliminary detection results by ELM of the data sequences are then used as input to a linear support vector machine for structured output to generate the final detection results. We test the proposed two-stage structured learning approach on a real-world dataset and show that the proposed approach outperforms the related machine learning methods.