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In many real-world applications such as image classification, labeled training examples are difficult to obtain while unlabeled examples are readily available. In this context, semi-supervised learning methods take advantage of both labeled and unlabeled examples. In this paper, a greedy graph-based semi-supervised learning (GGSL) approach is proposed for multi-class classification problems. The labels...
Dialogue act recognition is recognized as an important step for computers to understand human dialogues as it is closely related to the human intention. There are two main challenges in dialogue act recognition. Firstly, multimodal features should be taken into consideration, which include lexical, syntactic, prosodic cues, even facial appearance and gesture. Secondly, samples distribution in the...
This paper presents a Transductive Support Vector Machine (TSVM) with quasi-linear kernel based on a clustering assumption for semi-supervised classification. Since the potential separating boundary is located in low density area between classes, a modified density clustering method by considering label information is firstly introduced to extract the information of potential separating boundary in...
Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this task. However, the long time needed to train such deep networks is a major drawback. We tackled this problem by reusing a previously trained network...
Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF...
In this study, a lightweight kernel regression algorithm for embedded systems is proposed. In our previous study, we proposed an online learning method with a limited number of kernels based on a kernel regression model known as a limited general regression neural network (LGRNN). The LGRNN behavior is similar to that of k-nearest neighbors except for its continual interpolation between learned samples...
A new architecture based on the Multi-channel Convolutional Neural Network (MCCNN) is proposed for recognizing facial expressions. Two hard-coded feature extractors are replaced by a single channel which is partially trained in an unsupervised fashion as a Convolutional Autoencoder (CAE). One additional channel that contains a standard CNN is left unchanged. Information from both channels converges...
Accurate forecasting of upcoming trends in the capital markets is extremely important for algorithmic trading and investment management. Before making a trading decision, investors estimate the probability that a certain news item will influence the market based on the available information. Speculation among traders is often caused by the release of a breaking news article and results in price movements...
Multi-Column Deep Neural Networks achieve state of the art recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human accuracy. This performance is the result of averaging 11-layers deep networks with hundreds of maps per layer, trained on raw, distorted images to prevent them from overfitting. The entire framework runs on a normal desktop...
Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for intelligent robot. However, existing work neglects the intrinsic relation between different fingers which simultaneously contact the object. In this paper, a joint kernel sparse coding...
In this paper we propose a biometric solution for individual identification based on electroencephalography with classification using local probability centers. In our study, the electroencephalography signals of a subject are recorded from only one active channel Cz with eyes closed and without any external stimulations. The original signals are preprocessed by Haar wavelet transformation; then a...
This paper proposes novel algorithms for data-point and feature selection of motor imagery electroencephalographic signals for classifying motor plannings involved in car- driving including braking, acceleration, left steering control and right steering control. Variants of neural network classifiers such as linear support vector machines, and kernel-based support vector machines including radial...
Support Vector Data Description (SVDD) is a well-known supervised learning method for novelty detection purpose. For its classification task, SVDD requires a fully-labeled dataset. Nonetheless, contemporary datasets always consist of a collection of labeled data samples jointly a much larger collection of unlabeled ones. This fact impedes the usage of SVDD in the real-world problems. In this paper,...
This paper proposes a geometric way to construct a quasi-linear kernel by which a quasi-linear support vector machine (SVM) is performed. A quasi-linear SVM is a SVM with quasi-linear kernel, in which the nonlinear separation boundary is approximated by using multi-local linear boundaries with interpolation. However, the local linearity extraction for the composition of quasi-linear kernel is still...
Spectral clustering has become one of the main clustering methods and has a wide range of applications. Similarity measure is crucial to correct cluster separation for spectral clustering. Many existing spectral clustering algorithms typically measure similarity based on the undirected k-Nearest Neighbor (kNN) graph or Gaussian kernel function, which can not reveal the real clusters of not well-separated...
A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since...
Correntropy has been successfully applied in non-Gaussian signal processing, but the superior performance achieved is depends on appropriate selection of the kernel width. How to select a proper kernel width is a crucial problem in correntropy applications. In this paper, we propose an adaptive algorithm to update the kernel width, which is set at a maximum between the absolute value of instantaneous...
The aim of this paper is to evaluate the effectiveness of a class of data-driven physical models to represent both acoustic and high-speed video data of the voice production process. Voice production analysis through numerical models of the phonation process is nowday a mature research field, and reliable dynamical glottal models of different accuracy and complexity are available. Although they are...
In this work, we investigate the use of subspace methods as a representation for the human face-space and how to apply them to face detection for low resolution images (19 × 19 pixel images). We compare between different subspace paradigms, namely, principal component analysis (PCA), linear discriminant analysis (LDA) and kernel linear discriminant analysis (KLDA). We find that particularly the eigenface...
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