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Joint sparse representation (JSR) has been a popular technique for hyperspectral image classification, where a testing pixel and its spatial neighbors are simultaneously approximated by a sparse linear combination of all training samples, and the testing pixel is classified based on the joint reconstruction residual of each class. Due to the least-squares representation of the approximation error,...
There is always noise inside the digital images. Noise is an unwanted component of the image. The existence of noise in a face image can degrade the accuracy of a face recognition. Therefore, we need a proper method that can cope noise or restore the quality of the image. The best method to overcome noise in the image is to use smoothing (filter). In this research, we discuss some techniques to overcome...
Testing analog and mixed-signal circuits is a costly task due to the required test time targets and high end technical resources. Indirect testing methods partially address these issues providing an efficient solution using easy to measure CUT information. In this work, the pass/fail test regions are encoded using octrees in the measure space. These octrees, generated in the training phase, will serve...
A method based on sparse denoising autoencoder for denoising hybrid noises in image is proposed in this paper. The method is experimented on natural images and the performance is evaluated in terms of peak signal to noise ratio (PSNR). By specifically designing the training process of sparse denoising autoencoder, our model not only achieves good performance on single kind of noises, but also is relatively...
EEG based biometric system can be used for authentication, with advantages like confidentiality retention and forgery prevention. Signals which are taken from maximum brain regions show some sort of unique information that can be used for extracting the subject dependent pattern. This paper presents an approach to find the relationships among signals generated in different brain regions which give...
In this paper, we proposed a region-based approach for indoor localization based on the fingerprint of radio signal strength (RSS). Unlike conventional fingerprint-based methods that based on point information to infer user's location, we used region information for indoor localization. Our results show that the proposed system has better positioning accuracy and more robustness to signal noise and...
There have been considerable advances in multimedia recognition recently as powerful computing capabilities and large, representative datasets become ubiquitous. A fundamental assumption of traditional recognition techniques is that the data available for training are accurately labelled. Given the scale and diversity of web data, it takes considerable annotation effort to reduce label noise to acceptable...
A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based and resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. In the first...
In recent years recurrent neural network language models (RNNLMs) have been successfully applied to a range of tasks including speech recognition. However, an important issue that limits the quantity of data used, and their possible application areas, is the computational cost in training. A signi??cant part of this cost is associated with the softmax function at the output layer, as this requires...
Pitch information is an important cue for speech separation. However, pitch estimation in noisy condition is also a task as challenging as speech separation. In this paper, we propose a supervised learning architecture which combines these two problems concisely. The proposed algorithm is based on deep stacking network (DSN) which provides a method of stacking simple processing modules in building...
Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset classification applications. It has been broadly embedded in many applications due to its fast speed of computation and accuracy. How to make good use of machine learning techniques in Indoor Positioning System (IPS) is a hot research topic in recent years...
Online group shopping is feasible if the online store comprises useful products. The product needs to attract customer interest. The customer will have varied choices to choose from the available products. For feasibility of Online group shopping by customers the products have to be properly classified. The site offers the customer to form a group based on his/her interest. The site gives the best...
The aim of this paper is to describe the effect of training methods on the accuracy of PCA-KNN partial discharge (PD) classification model. This model used principal component analysis (PCA) combined with k-nearest neighbor (KNN) model, so called, PCA-KNN PD classification model for PD pattern classification. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air...
This paper addresses the problem of speech segregation by estimating the ideal binary mask (IBM) from noisy speech. Two methods will be compared, one supervised learning approach that incorporates a priori knowledge about the feature distribution observed during training. The second method solely relies on a frame-based speech presence probability (SPP) es-timation, and therefore, does not depend...
The use of word senses in place of surface word forms has been shown to improve performance on many computational tasks, including intelligent web search. In this paper we propose a novel approach to automatic discovery of word senses from raw text, a task referred to as Word Sense Induction (WSI). Almost all the WSI approaches proposed in the literature dealt with monolingual data and only very few...
A novel identification approach for identifying photographic images (PIM) and photorealistic computer graphics (PRCG) is proposed by using multifractal spectrum features of photo response non-uniformity noise (PRNU). 8 dimensions of mul-tifractal spectrum features of PRNU are extracted to represent the subtle differences between them, and the identification is carried out by using a support vector...
For the purpose of smooth human-robot interaction, a robot is supposed to be capable of semantically parsing the human instructions in a large scale. However, the existing supervised approaches to learning a large-scale semantic parser needs a good deal of training examples with annotations. The exhaustive cost of annotating enough sentences prevents them from learning such parser for interpreting...
In recently, there are many machine learning approaches have developed for intelligent control. One of these approaches is least squares-support vector machine regression (LS-SVMR). Besides, the robustness problem of the LS-SVMR among machine learning algorithms is importantly considered in recent years. Hence, for the robustness problem in LS-SVMR, a least trimmed squares support vector machine regression...
In this paper we are interested in exploiting self-similarity information for discriminative image denoising. Towards this goal, we propose a simple yet powerful denoising method based on transductive Gaussian processes, which introduces self-similarity in the prediction stage. Our approach allows to build a rich similarity measure by learning hyper parameters defining multi-kernel combinations. We...
Silhouette-based gait analysis is a well-established biometric approach for human identification. Over the years researchers have proposed a number of gait recognition approaches based on the entire silhouette of human body. These approaches are proven to give good recognition accuracies. However, the feature vector generation and subsequent classification depend on information extracted from the...
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