Supervised Feature Classification Research Articles

Back Propagation ELM based Multilayer Feature Extraction for Image and Text

ELM-based hierarchical learning framework for multilayer perceptron in which it has self-taught feature extraction followed by supervised feature classification and they are bridged by random initialized hidden weights is built in a multilayer manner. H-ELM training is divided into two separate phases: 1) unsupervised hierarchical feature representation and 2) supervised feature classification. A new H-ELM-based auto-encoder is developed to extract multilayer sparse features of the input data. The original ELM-based regression is performed for final decision making. H-ELM-based feature extraction and detection algorithms are developed for practical computer vision applications, such as object detection, recognition, and tracking. Since the supervised training is implemented by the original ELM, the unsupervised building blocks of the H-ELM architecture. The hidden layers of the framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. The advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed. To overcome this, in the proposed system, these issues are overcome by implementing with image and text using Back-Propagation–Extreme Learning Machine algorithm (BP-ELM) and then classified both text and image by using Support Vector Machine algorithm. Hence the proposed system detects the image and text in the form accuracy and efficiency of an image.

An effective hierarchical extreme learning machine based multimodal fusion framework

Abstract Deep learning has been successfully applied to multimodal representation learning. Similar with single modal deep learning method, such multimodal deep learning methods consist of a greedy layer-wise feedforward propagation and a backpropagation (BP) fine-tune conducted by diverse targets. These models have the drawback of time consuming. While, extreme learning machine (ELM) is a fast learning algorithm for single hidden layer feedforward neural network. And previous works has shown the effectiveness of ELM based hierarchical framework for multilayer perceptron. In this paper, we introduce an ELM based hierarchical framework for multimodal data. The proposed architecture consists of three main components: (1) self-taught feature extraction for specific modality by an ELM-based sparse autoencoder, (2) fused representation learning based on the features learned by previous step and (3) supervised feature classification based on the fused representation. This is an exact feedforward framework that once a layer is established, its weights are fixed without fine-tuning. Therefore, it has much better learning efficiency than the gradient based multimodal deep learning methods. We conduct experiments on MNIST, XRMB and NUS datasets, the proposed algorithm obtains faster convergence and achieves better classification performance compared with the other existing multimodal deep learning models.

Automatic diagnosis of pulmonary nodules using a hierarchical extreme learning machine model

To effectively improve the diagnosis of pulmonary nodules, this paper proposes a new automatic diagnosis method for pulmonary nodules based on a new hierarchical extreme learning machine (H-ELM) that can automatically carry out feature extraction, model training and pulmonary nodule detection. In our method, an adaptive histogram equalisation is used first to enhance contrast of the original pulmonary nodule image. The processed images are then input into an extreme learning machine (ELM)-based unsupervised multilayer auto-encoder to obtain more compact and meaningful high-level features of the pulmonary nodule image. Finally, supervised feature classification, which uses these high-level features of the pulmonary nodule as input data, is implemented using the ELM classifier. In the experiments, 2,800 pulmonary nodule images are used to validate the proposed method, and compared with existing pulmonary nodule diagnosis methods, our proposed method is more accurate and less time consuming and effectively avoids the complexity of manual feature extraction.

Extended Hierarchical Extreme Learning Machine with Multilayer Perceptron

The Deep Learning approach provides a high performance of classification, especially when invoking image classification problems. However, a shortcoming of the traditional Deep Learning method is the large time scale of training. The hierarchical extreme learning machine (H-ELM) framework was based on the hierarchical learning architecture of multilayer perceptron to address the problem. H-ELM is composed of two parts; the first entails unsupervised multilayer encoding, and the second is the supervised feature classification. H-ELM can give a higher accuracy rate than the traditional ELM. However, there still remains room to enhance its classification performance. This paper therefore proposes a new method termed the extending hierarchical extreme learning machine (EH-ELM), which extends the number of layers in the supervised portion of the H-ELM from a single layer to multiple layers. To evaluate the performance of the EH-ELM, the various classification datasets were studied and compared with the H-ELM and the multilayer ELM, as well as various state-of-the-art such deep architecture methods. The experimental results show that the EH-ELM improved the accuracy rates over most other methods.

Extreme Learning Machine for Multilayer Perceptron.

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via l1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.