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Variable selection is one of the most important contents of machine learning research and a large number of variable selection methods have been proposed at present. From deep learning perspective, we propose a new variable selection method based on multi-layer artificial neural networks in this paper. The method propagates errors gained form a trained network model through the network from output layer to input layer and the values gained by input layer give information about the relative contribution. The variables with big contribution value are selected, which we call neural inverse propagation (NIP). Specifically, after training a neural network, the gained errors between the actual network outputs and the desired outputs go through the network from top to down and arrive at the input layer. The absolute values gained by input units provide information about the relative importance (contribution) of input units, which is that the larger the value is, the more important the corresponding input variable is. We remove k variables corresponding to the first k minimum absolute value gained by input units to the predictive variables, to perform variable selection. The efficiency of the proposed method is illustrated through its application to classification and regression tasks on a number of synthetic datasets and a real-world image classification dataset. [ABSTRACT FROM AUTHOR]

Product quality is the lifeline of enterprise survival and development. With the rapid development of information technology, the semiconductor manufacturing process produces multitude of quality features. Due to the increasing quality features, the requirement on the training time and classification accuracy of quality prediction methods becomes increasingly higher. Aiming at realizing the quality prediction for semiconductor manufacturing process, this paper proposes a modified support vector machine (SVM) model based on feature selection, considering the high dimensional and nonlinear characteristics of data. The model first improves the Radial Basis Function (RBF) in SVM, and then combines the Duelist algorithm (DA) and variable neighborhood search algorithm (VNS) for feature selection and parameters optimization. Compared with some other SVM models that are based on DA, genetic algorithm (GA), and Information Gain algorithm (IG), the experiment results show that our DA-VNS-SVM can obtain higher classification accuracy rate with a smaller feature subset. In addition, we compare the DA-VNS-SVM with some common machine learning algorithms such as logistic regression, naive Bayes, decision tree, random forest, and artificial neural network. The results indicate that our model outperform these machine learning algorithms for the quality prediction of semiconductor. [ABSTRACT FROM AUTHOR]

Nowadays, it has become well known that efficient training of deep neural networks plays a vital role in various successful applications. To achieve this goal, it is impractical to use only one computer, especially when the scale of models is large and some efficient computing resources are available. In this paper, we present a distributed parallel computing framework for training deep belief networks (DBNs) by employing the great power of high-performance clusters (i.e., a system consists of many computers). Motivated by the greedy layer-wise learning algorithm of DBNs, the whole training process is divided layer by layer and distributed to different machines. At the same time, rough representations are exploited to parallelize the training process. By conducting experiments on several large-scale real datasets, the novel algorithms are shown to significantly accelerate the training speed of DBNs while achieving better or competitive prediction accuracy in comparison with the original algorithm. [ABSTRACT FROM AUTHOR]

Auto-encoders have been proved to be powerful unsupervised learning methods that able to extract useful features from input data or construct deep artificial neural networks by recent studies. In such settings, the extracted features or the initialized networks only learn the data structure while contain no class information which is a disadvantage in classification tasks. In this paper, we aim to leverage the class information of input to learn a reconstructive and discriminative auto-encoder. More specifically, we introduce a supervised auto-encoder that combines the reconstruction error and the classification error to form a unified objective function while taking the noisy concatenate data and label as input. The noisy concatenate input is constructed in such a method that one third has only original data and zero labels, one third has only label and zero data, the last one third has both original data and label. We show that the representations learned by the proposed supervised auto-encoder are more discriminative and more suitable for classification tasks. Experimental results demonstrate that our model outperforms many existing learning algorithms. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is an efficient learning algorithm of training single layer feed-forward neural networks (SLFNs). With the development of unsupervised learning in recent years, integrating ELM with autoencoder has become a new perspective for extracting feature using unlabeled data. In this paper, we propose a new variant of extreme learning machine autoencoder (ELM-AE) called generalized extreme learning machine autoencoder (GELM-AE) which adds the manifold regularization to the objective of ELM-AE. Some experiments carried out on real-world data sets show that GELM-AE outperforms some state-of-the-art unsupervised learning algorithms, including k -means, laplacian embedding (LE), spectral clustering (SC) and ELM-AE. Furthermore, we also propose a new deep neural network called multilayer generalized extreme learning machine autoencoder (ML-GELM) by stacking several GELM-AE to detect more abstract representations. The experiments results show that ML-GELM outperforms ELM and many other deep models, such as multilayer ELM autoencoder (ML-ELM), deep belief network (DBN) and stacked autoencoder (SAE). Due to the utilization of ELM, ML-GELM is also faster than DBN and SAE. [ABSTRACT FROM AUTHOR]