Multiclass Relevance Vector Machine Research Articles

Recognition of human activities using a multiclass relevance vector machine

We address the issue of human activity recognition by introducing the multiclass relevance vector machine (mRVM), the current state-of-the-art kernel machine learning technology given the multiclass classification problems (actually, activity recognition can commonly be viewed as a multiclass classification problem). Under our proposed recognition framework, the required procedure consists of three functional cascade modules: a. detecting the human silhouette blobs from the image sequence by the background subtraction method; b. extracting the shape and the motion features from the variation energy image (VEI); and c. sending the obtained features to the mRVM and recognizing the human activity. There are two types of mRVM: the constructive mRVM1 and the top-down mRVM2. We performed 10 times three-fold cross-validation on the Weizmann benchmark data set to examine the effectiveness of the proposed method. We also compared our method with other existing approaches, and the experimental results show that the proposed method offers superior performance. In summary, the mRVM, especially the mRVM2, has advantages both in terms of recognition rate and sparsity, along with a simple feature extraction process. The mRVM also significantly simplifies the classification process, by comparison with traditional binary-tree style multiclass classifiers.

Robust Hyperspectral Classification Using Relevance Vector Machine

The curse of dimensionality is the main reason for the computational complexity and the Hughes phenomenon in supervised hyperspectral classification. Previous studies seldom consider in a simultaneous fashion the real situation of insufficiency of available training samples, particularly for small land covers that often contain the key information of the scene, and the problem of complexity. In this paper, the capabilities of a feature reduction technique used for discrimination are combined with the advantages of a Bayesian learning-based probabilistic sparse kernel model, the relevance vector machine (RVM), to develop a new supervised classification method. In the proposed method, the hyperdimensional data are first transformed to a lower dimensionality feature space using the feature reduction technique to maximize separability between classes. The transformed data are then processed by a multiclass RVM classifier based on the parallel architecture and one-against-one strategy. To verify the effectiveness of the method, experiments were carried out on real hyperspectral data. The results are compared with the most efficient supervised classification techniques such as the support vector machine using appropriate performance indicators. The results show that the proposed method performs better than the other approaches particularly for small and scattered landcover classes which are harder to be precisely classified. In addition, this method has the advantages of low computational complexity and robustness to the Hughes phenomenon.

Intelligent fault diagnosis of rotating machinery based on multiple relevance vector machines with variance radial basis function kernel

Automatic and accurate fault diagnosis is very important for condition-based maintenance. In this study, an intelligent fault diagnosis method based on relevance vector machines (RVM) is proposed for automatic fault diagnosis of rotating machinery. First, the global optimal features from all node energies of full wavelet packet tree are obtained by combining wavelet packet transform with an improved Fisher feature selection method. Individual salient feature subsets are selected for each pair of classes separately. Then, RVM method is adopted to train the intelligent fault diagnosis model. The multi-class RVM classifier is constructed by combining several RVM binary classifiers using ‘max-probability-win’ strategy. Moreover, improved from Gaussian radial basis function, a new kernel function denoted variance radial basis function is developed and used for RVM to adaptively balance the difference between the scales of different features. The proposed method was carried out to develop a multi-class bearing fault diagnosis model under varying load conditions, resulting in high accuracy around 99.58 per cent. Experimental results demonstrate that the proposed method is promising for intelligent fault diagnosis of rotating machinery.

Application of kernel methods in signals modulation classification

A new approach to common signals classification of relevance vector machine (RVM) was presented and two signal classifiers based on kernel methods of support vector machine (SVM) and RVM were compared and analyzed. First several robust features of signals were extracted as the input of classifiers, then the kernel thought was used to map feature vectors impliedly to the high dimensional feature space, and multi-class RVM and SVM classifiers were designed to complete AM, CW, SSB, MFSK and MPSK signals recognition. Simulation result showed that when chose proper parameter, RVM and SVM had comparable accuracy but RVM had less learning time and basis functions. The classification speed of RVM is much faster than SVM.

Human Behavior Classification Using Multi-Class Relevance Vector Machine

Problem statement: In computer vision and robotics, one of the typical tasks is to identify specific objects in an image and to determine each object’s position and orientation relative to coordinate system. This study presented a Multi-class Relevance Vector machine (RVM) classification algorithm which classifies different human poses from a single stationary camera for video surveillance applications. Approach: First the foreground blobs and their edges are obtained. Then the relevance vector machine classification scheme classified the normal and abnormal behavior. Results: The performance proposed by our method was compared with Support Vector Machine (SVM) and multi-class support vector machine. Experimental results showed the effectiveness of the method. Conclusion: It is evident that RVM has good accuracy and lesser computational than SVM.

Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances

Recognition of power quality events by analyzing the voltage waveform disturbances is a very important task for the power system monitoring. This paper presents an integrated model for recognizing power quality disturbances (PQDs) using a new wavelet multi-class relevance vector machines (WMRVMs). The so-called relevance vector machines (RVMs) is an effective classification tool. The proposed method employs the wavelet transform techniques to extract the most important and significant feature from details and approximation waves. The obtained feature vectors are used for training the RVMs to classify the PQDs. Various transient events are tested, the result shows that the classifier can detect and classify different power quality signal correctly. Finally, sensitivity of the proposed algorithm under noise condition is investigated in this paper. Copyright © 2010 John Wiley & Sons, Ltd.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using an AE sensor with the test bearing operating at a constant loading (5 kN) and with a speed range from 20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine

This study concerns with fault diagnosis of low speed bearing using multi-class relevance vector machine (RVM) and support vector machine (SVM). A low speed test rig was developed to simulate various types of bearing defects associated with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired from the low speed bearing test rig using acoustic emission (AE) and accelerometer sensors under a constant load with different speeds. The aim of this study is to address the problem of detecting an incipient bearing fault and to find reliable methods for low speed machine fault diagnosis. In this paper, two methods of multi-class classification techniques for fault diagnosis through RVM and SVM are presented and the effectiveness of using AE and vibration signals due to low impact rate for low speed diagnosis. In the present study, component analysis was performed initially to extract the features and to reduce the dimensionality of original data features. The classification for fault diagnosis was also conducted using original data feature and without feature extraction. The result shows that multi-class RVM produces promising results and has the potential for use in fault diagnosis of low speed machine.