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Blind signal extraction is particularly attractive to solve signal mixture problems while only one or a few source signals are desired. Many desired biomedical signals exhibit distinct periods. A sequential method based on second order statistics is introduced in this paper. One can choose to recover one source signal or all signals in a specific order. The validity and performance of the proposed...
Contour detection is a fundamental problem in computer vision. However, there is still a considerable disparity between detection results and actual contours. To detect object-level contours on the basis of comprehensive analysis of potential edges, we present a deep-learning-based approach with a conditional random fields (CRF) model. We obtain the initial edgemap with a VGGNet-based model, and establish...
Multiple view data with different feature representations have widely arisen in various practical applications. Due to the information diversity, fusing multiview features is very valuable for classification purpose. In this paper, we propose a new multifeature fusion method called fractional-order discriminative multiview correlation projection (FDMCP), which is based on fractional-order scatter...
Feature fusion plays an important role in target recognition, especially when single sensor's recognition capability is limited under severe situations. In view of shortcomings of Multi-set Canonical Correlation Analysis (MCCA) and its supervised modified methods in using category information in fusion projection rule learning, a generalized discriminative learning version of MCCA, termed as GDMCCA,...
A novel scheme with deep cross-modal correlation learning is developed in this paper to facilitate more effective Sketch-based Image Retrieval (SBIR) for large-scale annotated images. It integrates the deep multimodal feature generation, deep cross-modal correlation learning and similarity search optimization through mining all the beneficial multimodal information sources in sketches and images,...
The type of evolutionary machine learning known as grammatical Evolution (GE) is currently receiving a great deal of attention. GE is particularly suitable for developing decision-tree classifiers because of a framework, in which candidate solutions are generated via production rules. Various decision-tree classifier methods based on GE have been proposed. In general, the performance of GE systems...
This work presents methods to automatically find optimal parameter settings for convolutional neural networks (CNNs) by using an evolutionary algorithm called particle swarm optimization (PSO). Even though the parameter space is extremely large (> 10 20), we experimentally show that a better parameter setting can be found for Alexnet configuration for five different image datasets. We have also...
This paper puts forward a testability modeling method for analog circuit fault prediction. It firstly gets the grey correlation entropy of each test point in the analog circuit. Then it treats each grey correlation entropy as a correlation coefficient to form the dependency matrix of testability. After that, according to the dependency matrix we get, the paper uses the method of PSO (Particle Swarm...
In this paper, we propose a technique for improving the feature extraction and classification stages in EEG-based Brain-Computer Interface (BCI) Systems. The problem can be formulated as Linear Matrix Inequalities (LMIs) and, therefore, be solved through robust computational tools. The idea is to represent the EEG signals using a sinusoidal signal basis in a given frequency range, and introducing...
This paper proposes a novel evolutionary approach to the optimal selection of electrodes as well as relevant EEG features for effective classification of cognitive tasks. The problem has been formulated in the framework of a single objective optimization problem with an aim to simultaneously satisfy three criteria. The first criterion deals with maximization of the correlation between the features...
In web topic detection, detecting “hot” topics from enormous User-Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from noisy images and short texts; and 2) uncertain roles of modalities where visual content is either highly or weakly relevant to textual cues due to less-constrained data. In this paper,...
In this paper, we propose a technique for improving the feature extraction and classification stages in EEG-based Brain-Computer Interface (BCI) Systems. The problem can be formulated as Linear Matrix Inequalities (LMIs) and, therefore, be solved through robust computational tools. The idea is to represent the EEG signals using a sinusoidal signal basis in a given frequency range, and introducing...
In industrial processes, the process operating performance may deteriorate with time from optimal performance due to process disturbances, noise, and other uncertainties, which prompts the development of process operating optimality assessment. In this study, a novel online operating optimality assessment method based on optimality related variations (ORV) is proposed for nonlinear industrial processes...
This paper investigates the problem of modeling Internet images and associated text for cross-modal retrieval tasks such as text-to-image search, and image-to-text search. Canonical correlation analysis (CCA), a classic two view approach for mapping text and image into a common latent space, does not make use of the semantic information of text and image pairs. We use CCA to map text, image and semantic...
In this paper, we propose a Fast Locality-constrained low-rank sparse coding for image classification. The low-rank coding seeks the homogeneousness and correlation of local features, encodes jointly and globally, based on the traditional low-rank coding, we incorporate locality constraints to enforce the local features sharing the same representation. Considering that the traditional low-rank coding...
Deep CCA is a recently proposed deep neural network extension to the traditional canonical correlation analysis (CCA), and has been successful for multi-view representation learning in several domains. However, stochastic optimization of the deep CCA objective is not straightforward, because it does not decouple over training examples. Previous optimizers for deep CCA are either batch-based algorithms...
We study Automatic Target Recognition (ATR) in infrared (IR) imagery from the perspective of feature fusion. The key to feature fusion is to take advantage of the discriminative and complementary information from different feature sets, which can be represented as internal (within each feature set) or external structures (across different feature sets). Traditional approaches tend to preserve either...
This paper develops a new elimination mechanism strategy to improve the performance of cooperative particle swarm optimizer with elimination mechanism (CPSO-EM) algorithm which is proposed to extract better vector elements by analyzing features of cooperative particle swarm optimizer (CPSO). The extracting method is simple and has the potential to be improved. The proposed cooperative particle swarm...
This paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback. The functioning of the signal processing technique has been...
Micro-blog has become the most popular information sharing tool in our daily life. The retweet behavior is a main method of information propagation in micro-blog. So there tweet number prediction not only is an interesting research topic, but also has much practical significance. However, most of current researches only regard this problem as a classification or regression problem, and they did not...
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