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Based on quaternion, an algorithm for color image de-noising has been proposed in this paper. According to the quaternion singular value decomposition theory, in a color image, the singular values on the diagonal matrix, which were obtained through QSVD, represent the color images in different channels. The additive noise of a color image can be eliminated effectively by keeping the proper singular...
Support vector machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on nu-transductive support vector machines for classification (nu-TSVC) and construct a new algorithm - Unconstrained nu-Transductive Support Vector Machines (Unu-TSVM). After researching on the special construction of primal problem in nu-TSVM, we transform...
To obtain the inverses of time-varying matrices in real time, a special kind of recurrent neural networks has recently been proposed by Zhang et al. It is proved that such a Zhang neural network (ZNN) could globally exponentially converge to the exact inverse of a given time-varying matrix. To find out the effect of time-derivative term on global convergence as well as for easier hardware-implementation...
In this paper, stochastic control of nonlinear state space models is discussed. After a brief review on nonlinear state space models, a multi layer perceptron (MLP) neural network is considered to represent the general structure of the controller. Then, an expectation maximization (EM) algorithm joint with the particle smoothing framework are proposed for updating parameters of the MLP network. The...
To localize a seen object, the superior colliculus of the barn owl integrates the visual and auditory localization cues which are accessed from the sensory system of the brain. These cues are formed as visual and auditory maps, thus the alignment between visual and auditory maps is very important for accurate localization in prey behavior. Blindness or prism wearing may disturb this alignment. The...
The support vector machine has been recently developed for blind equalization of constant modulus signals. In this paper we propose to use a v-support vector regressor (nu-SVR) for blindly equalizing multipath channels because of the high generalization ability of the SVR for short burst sequences. A weighted least square procedure is presented for solving the blind nu-SVR equalizer. The performance...
Spam messages pose a major threat to the usability of electronic mail. Spam wastes time and money for network users and administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium to distribute harmful code and/or offensive content. In this paper, we investigate the application of abductive learning in filtering out spam messages. We...
An image compression method combining discrete wavelet transform (DWT) and vector quantization (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate subbands (ten codebooks are generated using the Self Organizing Feature Map algorithm, which are then used in Vector Quantization, of the wavelet transformed subband images, i.e. one codebook for one...
Structured diagrams are very prevalent in many document types. Most people who need to create such diagrams use structured graphics editors such as Microsoft Visio. Structured graphics editors are extremely powerful and expressive but they can be cumbersome to use. We have shown through extensive timing experiments that structured diagrams drawn by hand will take only about 10% of the time it takes...
Early detection of a tumorpsilas site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. Here, we apply a neural network clustering theory, Fuzzy ART, to generate the division of cancer samples, which is useful...
The paper presents the application of a single-class Support Vector Machine (SVM) for localization of the focus region at the epileptic seizure on the basis of EEG registration. The diagnostic features used in recognition are derived from the directed transfer function description, determined for different ranges of EEG signals. The results of the performed numerical experiments for the localization...
Semi-supervised learning has received much attention recently. Co-training is a kind of semi-supervised learning method which uses unlabeled data to improve the performance of standard supervised learning algorithms. A novel co-training style algorithm, RASCO (for RAndom Subspace CO-training), is proposed which uses stochastic discrimination theory to extend co-training to multi-view situation. The...
This paper introduces a novel paradigm to impute missing data that combines a decision tree with an auto-associative neural network (AANN) based model and a principal component analysis-neural network (PCA-NN) based model. For each model, the decision tree is used to predict search bounds for a genetic algorithm that minimise an error function derived from the respective model. The modelspsila ability...
PCA-guided k-Means performs non-hierarchical hard clustering based on PCA-guided subspace learning mechanism in a batch process. Sequential Fuzzy Cluster Extraction (SFCE) is a procedure for analytically extracting fuzzy clusters one by one, and is useful for ignoring noise samples. This paper considers a hybrid concept of the two clustering approaches and proposes a new robust k-Means algorithm that...
The document similarity measure is a key point in textual data processing. It is the main responsible of the performance of a processing system. Since a decade, kernels are used as similarity functions within inner-product based algorithms such as the SVM for NLP problems and especially for text categorization. In this paper, we present a semantic space constructed from latent concepts. The concepts...
In many practical applications besides a small generalization error also the interpretability of classification systems is of great importance. There is always a tradeoff among these two properties of classifiers. The similarity measure in the input space as defined by one of the most powerful classifiers, the Random Forest (RF) algorithm, is used in this paper as basis for the construction of Generalized...
A new approach to construct the classifiers from imbalanced datasets is proposed by combining SMOTE (synthetic minority over-sampling technique) and Biased-SVM (biased support vector machine) approaches. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ldquonormalrdquo examples with only a small...
In this paper, an independent component analysis (ICA) based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the...
Invariance transformation (IT) is a rewarding technique to facilitate classification. But it is often difficult to derive its definition. This paper derives a local invariance transformation definition from SVM decision function. The corresponding IT-distance definition is consequently designed in both input space and feature space. And a classification algorithm based on IT and Nearest Neighbor rule...
Data representation methods related to ICA and sparse coding have successfully been used to model neural representation. However, they are highly abstract methods, and the neural encoding does not correspond to a detailed neuron model. This limits their power to provide deeper insight into the sensory systems on a cellular level. We propose here data representation where the encoding happens with...
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