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Rolling element bearings are among the most frequently encountered components in the majority of rotating machines. Thus, prognostic and health management (PHM) of rolling bearing plays an important role on the working status of the machine system. Remaining useful life (RUL) prediction is the core of PHM. It's well known that original auto-regression (AR) model is suitable for the prediction of linear...
Detection of faults in the early stages for rotating machinery is important for optimizing maintenance chores and avoiding severe damages to other parts. An approach based on Dictionary learning for sparse representation aiming at gearbox fault detection is proposed. A gearbox vibration signal database with 900 records considering the normal case and nine different faults is analyzed. A dictionary...
This paper proposes a sparse denoising algorithm based on noise image training dictionary using K-SVD algorithm to solve the problem that fixed dictionary sparsity is not ideal and the denoising quality is not high enough in image sparse denoising. The main task of K-SVD algorithm is to update the dictionary alternately during iteration process. We compared the image denoising performance of three...
The research of malicious comments in sina weibo is very important. Because a large number of malicious comments seriously undermine the user experience in sina weibo. Based on the malicious comments detection technology named semantic information, this paper gives a different technology which improves the process of malicious dictionary construction and the process of malicious comments detection...
We investigate the performances of fixed point implementation recursive approximations of Gabor filters in a texture classification framework based on a bag of words approach. The obtained results indicate that it is possible to obtain similar performances as in the case of using “premium” but very costly feature extractors.
This article gives a new procedure for designing dictionaries in order to represent signals sparsely. From the given set of training signals, the procedure learns to find a sparse representation of the signals by nuclear norm minimization. This method is closely related to the problem of low-rank matrix completion.
Zero-shot learning, a special case of unsupervised domain adaptation where the source and target domains have disjoint label spaces, has become increasingly popular in the computer vision community. In this paper, we propose a novel zero-shot learning method based on discriminative sparse non-negative matrix factorization. The proposed approach aims to identify a set of common high-level semantic...
Zero-shot learning for visual recognition has received much interest in the most recent years. However, the semantic gap across visual features and their underlying semantics is still the biggest obstacle in zero-shot learning. To fight off this hurdle, we propose an effective Low-rank Embedded Semantic Dictionary learning (LESD) through ensemble strategy. Specifically, we formulate a novel framework...
We propose to jointly learn a Discriminative Bayesian dictionary along a linear classifier using coupled Beta-Bernoulli Processes. Our representation model uses separate base measures for the dictionary and the classifier, but associates them to the class-specific training data using the same Bernoulli distributions. The Bernoulli distributions control the frequency with which the factors (e.g. dictionary...
The main challenge for anomaly detection in Self-Organizing Industrial Systems (SOIS) is the high degree of freedom of the system, which causes a state-space explosion. Since the system is free to choose at runtime any solution out of the vast amount of possible ones, to ensure that the production process is optimal at all times, classic anomaly detection techniques can not be used one-to-one in SOISs...
Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained...
Broad Learning System [1] proposed recently demonstrates efficient and effective learning capability. This model is also proved to be suitable for incremental learning algorithms by taking the advantages of random vector flat neural networks. In this paper, a modified BLS structure based on the K-means feature extraction is developed. Compared with the original broad learning system, acceptable performance...
Face recognition has been an important task in pattern recognition and computer vision. Recently, sparse representation has become a popular data representation method in face recognition field. Convolutional sparse coding, which replaces the linear combination of a set of dictionary atoms with the sum of s series of mapping term convoluted with the dictionary filters, was proposed to improve the...
Sparse representation (SR) based hyperspectral image (HSI) classification is a rapidly evolving research topic. How to construct an optimized dictionary to better characterize spectral-spatial features of HSI is an important problem. In this paper, a novel spectral-spatial online dictionary learning (SSODL) method for HSI classification is proposed. The main idea is to learn a complete and discriminative...
Sparse decomposition of ground penetration radar (GPR) signals facilitates the use of compressed sensing techniques for faster data acquisition and enhanced feature extraction for target classification. In this paper, we investigate use of an online dictionary learning (ODL) technique in the context of GPR to bring down the learning time as well as improve identification of abandoned anti-personnel...
An improved super-resolution image reconstruction algorithm based on dictionary-learning is studied for the time-consuming algorithms in the existing dictionary training process. In this paper, the reconstruction of image super resolution is realized from the compressed sensing theory. The image patches are conveyed by sparse linear representations with an over-complete dictionary. In the process...
Discriminative dictionary learning aims to learn a dictionary from training samples in order to improve the discriminative ability of their coding vectors. Gabor wavelets have recently been successfully applied for hyperspectral image (HSI) classification due to their ability to extract joint spatial and spectrum information. Due to the high discriminative power of Gabor features, an efficient method,...
In this paper, a new subpixel mapping approach for hyperspectral image is proposed, using a spatial-spectral endmember dictionary with collaborative representation (CR). Different from the classic approaches, the proposed approach employ several spatially closest training samples as the endmembers used for the representation of each mixed pixel, instead of the entire training set. Furthermore, the...
This paper presents a combination of machine learning and lexicon-based approaches for sentiment analysis of students feedback. The textual feedback, typically collected towards the end of a semester, provides useful insights into the overall teaching quality and suggests valuable ways for improving teaching methodology. The paper describes a sentiment analysis model trained using TF-IDF and lexicon-based...
Learning-based face super-resolution approaches rely on representative dictionary as self-similarity prior from training samples to estimate the relationship between the low-resolution (LR) and high-resolution (HR) image patches. The most popular approaches, learn mapping function directly from LR patches to HR ones but neglects the multi-layered nature of image degradation process (resolution down-sampling)...
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