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We present a method for vision-based speaker identification in a group conversation. The group context in the conversation is modeled by the integrated face direction of group members. Experimental results show that integrated face direction of group members is effective for speaker identification in a group.
With the successive increase in usage of vehicles, severe traffic congestion is on the rise. This in turn leads to increase in environmental pollution and accidents which ultimately affects the safety, time consumed and money spent of the transport users. The solution to this critical problem is traffic flow prediction depending on which traffic control measures and traffic management can be done...
In this paper, we describe a one-class classification method based on Support Vector Data Description, which exploits multiple graph structures in its optimization process. We derive in a generic solution which can be employed for supervised one-class classification tasks. The devised method can produce linear or non-linear decision functions, depending on the adopted kernel function. In our experiments,...
Although the mobile head-mounted gaze tracker (HMGT) has gained its great success in human-machine interactions, the real implementation of HMGT still poses several significant challenges. The parallax error and the tedious calibration procedure, as two of these challenges, will be addressed in our proposed two-step calibration method. In the first step, instead of fixating at several pre-defined...
Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem...
In this paper, we focus on the classification of neutral and stressed speech. The parameters representing airflow patterns in physiological system are achieved using a physical model. Speech features were modeled using Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). A comparison is made of different classifiers to determine their performance in stressed speech classification. Results...
In real applications of one class classification, new features may be added due to some practical or technical reason. While lacking of representative samples for the new features, multi-task learning idea could be used to bring some information from the former learning model. Based on the above assumption, a new multi-task learning approach is proposed to deal with the training of the updated system...
Training kernel SVM on large datasets suffers from high computational complexity and requires a large amount of memory. However, a desirable property of SVM is that its decision function is solely determined by the support vectors, a subset of training examples with non-vanishing weights. This motivates a novel efficient algorithm for training kernel SVM via support vector identification. The efficient...
In modern cognitive ratio systems, the spectrum is becoming increasingly crowded and expensive; thus spectrum sensing becomes more important than ever before. Traditional spectrum sensing assumes Gaussian noise (or of other given distributions) in general. However when secondary users (SUs) have no prior information about the measurement distributions, the spectrum sensing schemes assuming given distribution...
Many real-world applications exhibit scenarios where distributions represented by training and test data are not similar, but related by a covariate shift, i.e., having equal class conditional distribution with unequal covariate distribution. Traditional data mining techniques suffer to learn a good predictive model in the presence of covariate shift. Recent studies have proposed approaches to address...
Feature selection (FS) has proven to be useful to improve the generalization performance of classifiers. For applications with a small number of instances but a large number of input features, FS methods based on single classifier evaluation are subject to instability.We propose a new FS algorithm based on SVM ensemble learning. First, an ensemble of SVM classifiers are trained with re-sampled subsets...
Limited access to supervised information may forge scenarios in real-world data mining applications, where training and test data are interconnected by a covariate shift, i.e., having equal class conditional distribution with unequal covariate distribution. Traditional data mining techniques assume that both training and test data represent an identical distribution, therefore suffer in presence of...
The evacuation of children and the elderly from disaster areas is sometimes difficult. This study aims to use a vibration sensor to estimate situations involving people who remain in a devastated building. This paper proposes a method to estimate the attributes of the people, such as their age or sex, based on the vibration data produced by their footsteps. The vibration data obtained through sensors...
In this paper, we describe a face verification method which is based on non-linear class-specific discriminant subspace learning. We follow the Kernel Spectral Regression approach to this end and employ a prototype-based approximate kernel regression scheme in order to scale the method for large-scale nonlinear discriminant learning. Experiments on two publicly available facial image databases show...
Sufficient dimension reduction (SDR) is a popular framework for supervised dimension reduction, aiming at reducing the dimensionality of input data while information on output data is maximally maintained. On the other hand, in many recent supervised classification learning tasks, it is conceivable that the balance of samples in each class varies between the training and testing phases. Such a phenomenon,...
Online kernel-based dictionary learning (DL) algorithms are considered, which perform DL on training data lifted to a high-dimensional feature space via a nonlinear mapping. Compared to batch versions, online algorithms require low computational complexity, essential for processing the Big Data, based on the stochastic gradient descent method. However, as with any kernel-based learning algorithms,...
Forecasting electricity price allows market participants to make informed and sound decisions. Selecting the best training variables is often involved in forecasting in order to obtain optimal prediction. Support Vector Regression (SVR) provides an effective method to fit data and find minimal risk slack variables around a fit line. The best fit depends on the selected input feature set and the tuning...
Sensor models, which specify the distribution of sensor observations, are a widely used and integral part of robotics algorithms. Observation distributions are commonly approximated by parametric models, which are limited in their expressiveness, and may require careful design to suit an application. In this paper, we propose nonparametric distribution regression as a procedure to model sensors. It...
With the advent of large numbers of data and a large number of samples, the traditional support vector machine algorithm is not applicable because of it's too much memory overhead and time overhead. Traditional parallel SVM based on MapReduce is to separate the train data into multiple sub-training sets on MapReduce-based model, these sub-datasets are trained by SVM, and then, get the support vectors...
The computational complexity of kernel methods grows at least quadratically with respect to the training size and hence low rank kernel approximation techniques are commonly used. One of the most popular approximations is constructed by sub-sampling the training data. In this paper, we present a sampling algorithm called Enhanced Distance Subset Approximation (EDSA) based on a novel kernel function...
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