Set Of Dictionaries Research Articles

Rolling Bearing Performance Degradation Assessment Based on Convolutional Sparse Combination Learning

A novel bearing performance degradation assessment method based on convolutional sparse coding and combination learning is proposed in this paper, which can avoid the impact of the traditional features on the assessment results under complex operation conditions. The vibration signal can be decomposed into the convolution of the kernel sets and their corresponding sparse solutions using convolutional sparse coding. The learned kernel sets based on the training signal samples are the comprehensive embodiment of the information related to the operational complexity and the bearing healthy state, and the corresponding sparse solutions indicate the energy of the kernel activation. Asimulation experiment of bearing signal proves that the activation energy of the kernels, which are more related to bearing healthy, will rise with the increase of the bearing degradation degree; meanwhile, the error of reconstructed signal based on the learned kernel sets and original signal will decrease since the description effect of convolutional sparse coding to the signal will be enhanced with the periodic strengthen of signal caused by the fault. Thus, an index based on the kernel sparse norms and the errors between the original and reconstructed signals has been proposed to evaluate the degradation degree of the bearing in this paper. On the other hand, combination learning will be fused into the convolutional sparse coding to improve the real-time performance of the index calculation and obtain the best assessment result in the testing part by dividing the kernel dictionary set into multiple sub-dictionaries. A bearing run-to-fail experiment is analyzed to verify the validity of the proposed method. The testing results show that the proposed assessment index can clearly detect the initial fault, serve fault and failure of the bearing in the whole life, and the proposed method is more self-adaptive and sensitive to the fault degree. In addition, it is verified that the time consumption of the combination dictionaries is less than that of a single dictionary set.

$\ell_P$ Norm Independently Interpretable Regularization Based Sparse Coding for Highly Correlated Data

Sparse coding, which aims at finding appropriate sparse representations of data with an overcomplete dictionary set, is a well-established signal processing methodology and has good efficiency in various areas. The varying sparse constraint can influence the performances of sparse coding algorithms greatly. However, commonly used sparse regularization may not be robust in high-coherence condition. In this paper, inspired from independently interpretable lasso (IILasso), which considers the coherence of sensing matrix columns in constraint to implement the strategy of selecting uncorrelated variables, we propose a new regularization by introducing $\ell _{p}$ norm $(0 into the regularization part of IILasso. The new regularization can efficiently enhance the performances in obtaining sparse and accurate coefficient. To solve the optimization problem with the new regularization, we propose to use the coordinate descent algorithm with weighted $\ell _{1}$ norm, named independently interpretable weighted lasso (IIWLasso), and the proximal operator, named independently interpretable iterative shrinkage thresholding algorithm (II-ISTA) and independently interpretable proximal operator for $\ell _{\frac {2}{3}}$ norm regularization (II2/3PO). We present synthetic data experiments and gene expression data experiments to validate the performance of our proposed algorithms. The experiment results show that all independently interpretable algorithms can perform better than their original ones in different coherence conditions. Among them, IIWLasso can obtain relatively best performance both in relative norm error and support error of synthetic data and misclassification error of tenfold cross-validating gene expression data.

A dictionary learning algorithm for multi-subject fMRI analysis based on a hybrid concatenation scheme

Analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects is at the heart of many medical imaging studies, and recently, the approaches based on dictionary learning (DL) are noted as promising solutions to the problem. However, the DL-based methods for fMRI analysis proposed to date do not naturally extend to multi-subject analysis. In this paper, we propose a DL algorithm for multi-subject fMRI data analysis which is derived using a hybrid (temporal and spatial) concatenation scheme. It differs from existing DL methods in both its sparse coding and dictionary update stages. It has the advantage of learning a dictionary common to all subjects as well as a set of subject-specific dictionaries, as a result, it is able to generate both group-level spatial activation maps as well as group-level temporal dynamics, which are particularly attractive for task-based fMRI studies. In addition, by simultaneously learning multiple sub-specific dictionaries, it also provides us with unique sub-specific features as well. Performance of the proposed DL method is illustrated using simulated and real fMRI datasets. The results show that it can successfully extract common as well as sub-specific latent components.

Data Driven Automatic Modulation Classification via Dictionary Learning

Automatic modulation classification (AMC) is the process of identifying the modulation format of the received signal. It is generally a difficult task due to the limited knowledge of the signal. In this letter, we propose a data driven dictionary-learning-based AMC framework, where we first use the known training signals to train the dictionary set and then classify the unknown modulation format via certain sparse representations, for which we design a dictionary-learning-based algorithm called block coordinate descent dictionary learning. Simulation results show that the proposed method out performs other existing approaches, achieving higher accuracy with a less training time.

Defocus Blur Detection and Estimation from Imaging Sensors.

Sparse representation has been proven to be a very effective technique for various image restoration applications. In this paper, an improved sparse representation based method is proposed to detect and estimate defocus blur of imaging sensors. Considering the fact that the patterns usually vary remarkably across different images or different patches in a single image, it is unstable and time-consuming for sparse representation over an over-complete dictionary. We propose an adaptive domain selection scheme to prelearn a set of compact dictionaries and adaptively select the optimal dictionary to each image patch. Then, with nonlocal structure similarity, the proposed method learns nonzero-mean coefficients’ distributions that are much more closer to the real ones. More accurate sparse coefficients can be obtained and further improve the performance of results. Experimental results validate that the proposed method outperforms existing defocus blur estimation approaches, both qualitatively and quantitatively.

External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising.

Most of existing image denoising methods learn image priors from either external data or the noisy image itself to remove noise. However, priors learned from external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex, which is hard to be described by simple distributions such as Gaussian distribution, making real-world noisy image denoising a very challenging problem. We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real-world noisy image denoising. We first learn external priors from an independent set of clean natural images. With the aid of learned external priors, we then learn internal priors from the given noisy image to refine the prior model. The external and internal priors are formulated as a set of orthogonal dictionaries to efficiently reconstruct the desired image. Extensive experiments are performed on several real-world noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real-world noisy images.

Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength

Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system interprets what a person is doing by deciphering signal fluctuations using radio-frequency identification (RFID) technology and machine learning algorithms. To deal with noisy, streaming, and unstable RFID signals, we develop a compressive sensing, dictionary-based approach that can learn a set of compact and informative dictionaries of activities using an unsupervised subspace decomposition. In particular, we devise a number of approaches to explore the properties of sparse coefficients of the learned dictionaries for fully utilizing the embodied discriminative information on the activity recognition task. Our approach achieves efficient and robust activity recognition via a more compact and robust representation of activities. Extensive experiments conducted in a real-life residential environment demonstrate that our proposed system offers a good overall performance and shows the promising practical potential to underpin the applications for the independent living of the elderly.

Lemmatization with reversed dictionary and fuzzy sets

This paper deals with the problem of lemmatization of unknown words in Russian and German. For this purpose, the improved analogy method is used. The analogy method being built around reverse dictionary is very efficient and simple to realize. Adopting fuzzy sets for improving the analogy method is described in this paper. The fuzzy set is a good tool for modelling continual properties of a natural language. It can be implemented for lemmatization as a convenient tool to summarize information that is obtained from different sources. The paper contributes to solving the problem of increasing the accuracy of unknown words analysis.

Deep Neural Network Structured Sparse Coding for Online Processing

Sparse coding, which aims at finding appropriate sparse representations of data with an overcomplete dictionary set, has become a mature class of methods with good efficiency in various areas, but it faces limitations in immediate processing such as real-time video denoising. Unsupervised deep neural network structured sparse coding (DNN-SC) algorithms can enhance the efficiency of iterative sparse coding algorithms to achieve the goal. In this paper, we first propose a sparse coding algorithm by adding the idea “weighted” in the iterative shrinkage thresholding algorithm (ISTA), named WISTA, which can enjoy the benefit of the $l_{p}$ norm $(0 sparsity constraint. Then, we propose two novel DNN-SC algorithms by combining deep learning with WISTA and the iterative half thresholding algorithm (IHTA), which is the $l_{0.5}$ norm sparse coding algorithm. Furthermore, we present that by changing the loss function, the DNN can be learned supervisedly and unsupervisedly. Unsupervised learning is the key to ensure the DNN to be learned online during processing, which enables the use of the DNN-SC algorithms in applications lacking labels for signals. Synthetic data experiments show that WISTA can outperform ISTA and IHTA. Moreover, the DNN-structured WISTA can successfully achieve converged results of WISTA. In real-world data experiments, the procedure of utilizing DNN-SC algorithms in image denoising is first presented. All DNN-SC algorithms can accelerate at least 45 times while maintaining PSNR results compared with their corresponding sparse coding algorithms. Finally, the strategy of utilizing DNN-SC algorithms in real-time video denoising is presented. The video-denoising experiments show that the DNN-structured ISTA and WISTA can conduct real-time video denoising for 25 frames/s $360\times 480$ pixels gray-scaled videos.

Tensor-Based Sparse Representation Classification for Urban Airborne LiDAR Points

The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. In many methods, only the features of each point are used, regardless of their spatial distribution within a certain neighborhood. This paper proposes a tensor-based sparse representation classification (TSRC) method for airborne LiDAR (Light Detection and Ranging) points. To keep features arranged in their spatial arrangement, each LiDAR point is represented as a 4th-order tensor. Then, TSRC is performed for point classification based on the 4th-order tensors. Firstly, a structured and discriminative dictionary set is learned by using only a few training samples. Subsequently, for classifying a new point, the sparse tensor is calculated based on the tensor OMP (Orthogonal Matching Pursuit) algorithm. The test tensor data is approximated by sub-dictionary set and its corresponding subset of sparse tensor for each class. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on eight real LiDAR point clouds whose result shows that objects can be distinguished by TSRC successfully. The overall accuracy of all the datasets is beyond 80% by TSRC. TSRC also shows a good improvement on LiDAR points classification when compared with other common classifiers.

Dissimilarity-Weighted Sparse Representation for Hyperspectral Image Classification

To improve the capability of a traditional sparse representation-based classifier (SRC), we propose a novel dissimilarity-weighted SRC (DWSRC) for hyperspectral image (HSI) classification. In particular, DWSRC computes the weights for each atom according to the distance or dissimilarity information between the test pixel and the atoms. First, a locality constraint dictionary set is constructed by the Gaussian kernel distance with a suitable distance metric (e.g., Euclidean distance). Second, the test pixel is sparsely coded over the new weighted dictionary set based on the 11-norm minimization problem. Finally, the test pixel is classified by using the obtained sparse coefficients with the minimal residual rule. Experimental results on two widely used public HSIs demonstrate that the proposed DWSRC is more efficient and accurate than other state-of-the-art SRCs.

Specialised Lexicography for Learners: Specific Proposals for the Construction of Pedagogically-oriented Printed Business Dictionaries

The function theory of lexicography argues that specialised lexicographical products must help learners to transform their information needs into aspects of knowledge of the discipline, and of its discursive properties. Lexicographers, then, must combine information and data access with the user’s need for information and knowledge. To achieve this aim they need to devise theories providing solutions to different lexicographical problems. One such theory has recently been proposed by Tarp (2008), who claims that there are four categories which are central to a general theory of learner’s lexicography: users, user situation, user needs, and dictionary assistance. This paper focuses on dictionary assistance and addresses several lexicographical issues connected with polysemy: the selection of the lemmata of some printed English-Spanish/Spanish-English business dictionaries, their entry structures, sense differentiation, and sense ordering. The analysis leads the author to discuss some proposals with the aim of making business dictionaries more pedagogically oriented, and to include a set of principles pedagogically-oriented business dictionaries must have. They are illustrated in a model entry which has been compiled by rearranging one of the entries studied according to the proposals and principles previously discussed.

Learning local dictionaries and similarity structures for single image super-resolution

The central task of reconstruction-based single image super-resolution (SR) approaches is to design an effective prior to well pose the solution to unknown up-sampled image. In this paper, we present a novel single image SR method by learning a set of local dictionaries and non-local similar structures from the input low-resolution (LR) image itself. The local dictionaries are learned by segmenting structurally different regions into different clusters and then training an individual dictionary for each cluster. With the learned dictionaries and similar information, each HR pixel in the expected HR image is estimated as the weighted average of a non-local dictionary (NLD)-based regression which assembles the local structural regularity and the non-local similar redundancies. We further transform the proposed NLD-based regression model into a unified regularization term for a maximum a posteriori probability (MAP) based SR framework. Thorough experimental results carried out on five publicly available datasets indicate that the proposed SR method is promising in producing high-quality images with finer details and sharper edges in terms of both quantitative and perceptual quality assessments.

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

Sparse coding with memristor networks.

Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary.

Kernel-Based Multiview Joint Sparse Coding for Image Annotation

It remains a challenging task for automatic image annotation problem due to the semantic gap between visual features and semantic concepts. To reduce the gap, this paper puts forward a kernel-based multiview joint sparse coding (KMVJSC) framework for image annotation. In KMVJSC, different visual features as well as label information are considered as distinct views and are mapped to an implicit kernel space, in which the original nonlinear separable data become linearly separable. Then, all the views are integrated into a multiview joint sparse coding framework aiming to find a set of optimal sparse representations and discriminative dictionaries adaptively, which can effectively employ the complementary information of different views. An optimization algorithm is presented by extending K-singular value decomposition (KSVD) and accelerated proximal gradient (APG) algorithms to the kernel multiview framework. In addition, a label propagation scheme using the sparse reconstruction and weighted greedy label transfer algorithm is also proposed. Comparative experiments on three datasets have demonstrated the competitiveness of proposed approach compared with other related methods.

Single image super-resolution by directionally structured coupled dictionary learning

In this paper, a new algorithm is proposed based on coupled dictionary learning with mapping function for the problem of single-image super-resolution. Dictionaries are designed for a set of clustered data. Data is classified into directional clusters by correlation criterion. The training data is structured into nine clusters based on correlation between the data patches and already developed directional templates. The invariance of the sparse representations is assumed for the task of super-resolution. For each cluster, a pair of high-resolution and low-resolution dictionaries are designed along with their mapping functions. This coupled dictionary learning with a mapping function helps in strengthening the invariance of sparse representation coefficients for different resolution levels. During the reconstruction phase, for a given low-resolution patch a set of directional clustered dictionaries are used, and the cluster is selected which gives the least sparse representation error. Then, a pair of dictionaries with mapping functions of that cluster are used for the high-resolution patch approximation. The proposed algorithm is compared with earlier work including the currently top-ranked super-resolution algorithm. By the proposed mechanism, the recovery of directional fine features becomes prominent.

Vehicle Detection in High-Resolution Aerial Images Based on Fast Sparse Representation Classification and Multiorder Feature

This paper presents an algorithm for vehicle detection in high-resolution aerial images through a fast sparse representation classification method and a multiorder feature descriptor that contains information of texture, color, and high-order context. To speed up computation of sparse representation, a set of small dictionaries, instead of a large dictionary containing all training items, is used for classification. To extract the context information of a patch, we proposed a high-order context information extraction method based on the proposed fast sparse representation classification method. To effectively extract the color information, the RGB color space is transformed into color name space. Then, the color name information is embedded into the grids of histogram of oriented gradient feature to represent the low-order feature of vehicles. By combining low- and high-order features together, a multiorder feature is used to describe vehicles. We also proposed a sample selection strategy based on our fast sparse representation classification method to construct a complete training subset. Finally, a set of dictionaries, which are trained by the multiorder features of the selected training subset, is used to detect vehicles based on superpixel segmentation results of aerial images. Experimental results illustrate the satisfactory performance of our algorithm.

Cross-View Action Recognition via Transferable Dictionary Learning.

Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

Cancer claims and registry data: A California linkage for quality measurement.

296 Background: Breast and colon are 2 of the most prevalent cancers in California according to the California Cancer Registry (CCR). Health claims data contains a wealth of information on diagnoses and treatment but lacks clinical information critical for evaluating care. Registry data contains key clinical information, but often does not contain the complete treatment regimen. This project aims to assess the feasibility of linking commercial claims data to population-based cancer registry data, and to use the linked data to examine variation in quality measures at the regional and physician organization (PO) levels. Methods: Nine NQF endorsed breast and colon cancer measures were selected and measure specifications with code sets were developed. Data on members identified with a diagnosis of breast and/or colon cancer in claims (2009-2012) from 4 commercial HMO health plans was linked with CCR data. Results were generated at the regional and PO levels. Results: The feasibility test was a success; CCR and claims data was linked for 8,757 individuals, and the data sets proved complementary. Rates relying on the linked dataset were typically higher than when either data source was used alone. For example, see the Table. Performance was strong across 8 of 9 measures with scores ranging from 80-98%. The 9th measure, which relied only on CCR data, was 51%. PO level measurement was limited by small sample sizes, but where sample size was adequate, a significant amount of variation in performance across POs was found. Conclusions: The project showed that linkage of claims and registry data is feasible and that the linked dataset supports more robust assessment of the quality of cancer care. Further, the data dictionary and programmable code sets that were developed are available to other entities interested in creating linkages. With larger sample size, robust benchmarks could be established. Linked registry and claims data could be used for additional research in areas such as compliance with clinical guidelines and examining patterns of treatment. [Table: see text]