Multiple Dictionaries Research Articles

Online review analysis-based multi-criteria decision-making for evaluating patient satisfaction: A case study of the Haodf website

The online reviews provided by patients contain many aspects of patient satisfaction (PS). An accurate understanding of PS can help hospitals and doctors quickly find the direction of medical service improvement and help patients select appropriate doctors. However, online reviews are texts in which patients show true feelings without constraints. Therefore, identifying, measuring, and representing PS are difficult. To solve these problems, we propose the online review analysis-based multi-criteria decision-making (MCDM) method. First, an aspect extraction method integrating dependency parsing and attention-based aspect extraction (ABAE) is proposed, and nine criteria for PS evaluation are extracted from the online reviews of the Haodf website. Second, a sentiment analysis method based on multiple dictionaries and dependency relations is developed to measure PS under each criterion in reviews. Then, an MCDM method based on a probabilistic linguistic term set representing PS is used to assess PS when considering patients’ loss aversion. Finally, the proposed method is verified in the evaluation of lung cancer patients’ satisfaction with doctors. The results show that our extracted criteria have higher coherence and accuracy compared to those extracted by other aspect extraction methods, and the proposed online review analysis-based MCDM method outperforms state-of-the-art methods in PS identification, measurement, and representation.

Multiscale reconstruction of porous media based on multiple dictionaries learning

Digital modeling of the microstructure is important for studying the physical and transport properties of porous media. Multiscale modeling for porous media can accurately characterize large-pores and fine-pores in a large-FoV (field of view) high-resolution three-dimensional pore structure model. This paper proposes a multiscale reconstruction algorithm based on multiple dictionaries learning, in which edge patterns and fine-pore patterns from homology high-resolution pore structure are introduced into low-resolution pore structure to build a fine multiscale pore structure model. The qualitative and quantitative comparisons of the experimental results show that the results of multiscale reconstruction are similar to the real high-resolution pore structure in terms of complex pore geometry and pore surface morphology. The geometric, topological and permeability properties of multiscale reconstruction results are almost identical to those of the real high-resolution pore structures. The experiments also demonstrate the proposal algorithm is capable of multiscale reconstruction without regard to the size of the input. This work provides an effective method for fine multiscale modeling of porous media.

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning.

A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields. Its aim is to improve the under-determination of the inverse problem by the sparse representation of the sound slowness signal (i.e., reciprocal of sound velocity). In the MDL algorithm, the K-SVD dictionary learning algorithm is used to construct corresponding sparse dictionaries for sound slowness signals of different types of temperature fields; the KNN peak-type classifier is employed for the joint use of multiple dictionaries; the orthogonal matching pursuit (OMP) algorithm is used to obtain the sparse representation of sound slowness signal in the sparse domain; then, the temperature distribution is obtained by using the relationship between sound slowness and temperature. Simulation and actual temperature distribution reconstruction experiments show that the MDL algorithm has smaller reconstruction errors and provides more accurate information about the temperature field, compared with the compressed sensing and improved orthogonal matching pursuit (CS-IMOMP) algorithm, which is an algorithm based on compressed sensing and improved orthogonal matching pursuit (in the CS-IMOMP, DFT dictionary is used), the least square algorithm (LSA) and the simultaneous iterative reconstruction technique (SIRT).

Retraction Note: Single image shadow detection and removal based on feature fusion and multiple dictionary learning

Retraction Note: Single image shadow detection and removal based on feature fusion and multiple dictionary learning

Fast Sparsity-Assisted Signal Decomposition With Nonconvex Enhancement for Bearing Fault Diagnosis

Sparsity-assisted signal decomposition (SASD) based on morphological component analysis (MCA) for bearing fault diagnosis has been studied in-depth. However, existing algorithms often use different combinations of representation dictionaries and priors, leading to difficult dictionary choice and high computational complexity. This article aims to develop a fast sparsity-assisted algorithm to decompose a vibration signal into discrete frequency and impulse components for bearing fault diagnosis. We introduce the morphological discrimination of discrete frequency and impulse components in time and frequency domains, respectively, for the first time. To use this morphological discrimination, we establish a fast SASD based on MCA with nonconvex enhancement. We further prove the necessary and sufficient condition to guarantee the convexity and use the majorization minimization algorithm to derive a fast solver. The proposed algorithm not only has low computational complexity, but also avoids choosing multiple dictionaries as well as underestimation of impulse features. Furthermore, an adaptive parameter selection algorithm to set parameters of our algorithm is designed for real applications. The effectiveness of fast SASD and its adaptive variant is verified by both simulation studies and bearing diagnosis cases. The source codes will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZhaoZhibin/Fast_SASD</uri> .

Editing Compression Dictionaries toward Refined Compression-Based Feature-Space

This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects x and y through the compression ratio to compress x with y (’s dictionary). Specifically, we focus on the known framework called PRDC, which represents an object x as a compression-ratio vector (CV) that lines up the compression ratios after x is compressed with multiple different dictionaries. By representing an object x as a CV, PRDC makes it possible to apply vector-based pattern recognition techniques to the compression-based pattern recognition. For PRDC, the dimensions, i.e., the dictionaries determine the quality of the CV space. This paper presents a practical technique to modify the chosen dictionaries in order to improve the performance of pattern recognition substantially: First, in order to make the dictionaries independent from each other, our method leaves any word shared by multiple dictionaries in only one dictionary and assures that any pair of dictionaries have no common words. Next, we transfer words among the dictionaries, so that all the dictionaries may keep roughly the same number of words and acquire the descriptive power evenly. The application to real image classification shows that our method increases classification accuracy by up to 8% compared with the case without our method, which demonstrates that our approach to keep the dictionaries independent is effective.

Hyperspectral sparse unmixing based on multiple dictionary pruning

ABSTRACT When a spectral library is known, hyperspectral sparse unmixing could obtain the abundance images, which estimate the fractional proportions in each pixel in a hyperspectral image scene. Dictionary pruning (DP) methods furtherly improve the performance of the hyperspectral sparse unmixing by reducing the spectral dictionary to a smaller subset. However, the current hyperspectral sparse unmixing algorithms usually only use a single DP method to obtain the subset, which affects the sparse unmixing results. In this study, we propose a multiple dictionary pruning (MDP) method to improve the performance of hyperspectral sparse unmixing algorithms, making them more accurate. MDP consists of three DP methods, namely, spectral angle mapping (SAM), spectral information divergence (SID) and robust multiple signal classification (RMUSIC). In MDP, we first make a difference between SAM and SID to produce the first level subset named as SS, reducing the impact of non-target information spectrums on the accuracy of sparse unmixing. We then use the RMUSIC to handle the SS to obtain the second level subset named as SSTR, reducing the endmember variability and calibration errors. Finally, we introduce the more accurate subset SSTR into the current hyperspectral sparse unmixing algorithms to produce the sparse unmixing results. Experimental results on two simulated data sets and one real data set show that the proposed MDP method is superior to the traditional DP methods.

Four best practices for measuring news sentiment using ‘off-the-shelf’ dictionaries: a large-scale p-hacking experiment

Abstract We examined the validity of 37 sentiment scores based on dictionary-based methods using a large news corpus and demonstrated the risk of generating a spectrum of results with different levels of statistical significance by presenting an analysis of relationships between news sentiment and U.S. presidential approval. We summarize our findings into four best practices: 1) use a suitable sentiment dictionary; 2) do not assume that the validity and reliability of the dictionary is ‘built-in’; 3) check for the influence of content length and 4) do not use multiple dictionaries to test the same statistical hypothesis.

Multimode Process Monitoring and Mode Identification Based on Multiple Dictionary Learning

In modern industrial systems, the processes often operate under different modes. Potential fault diagnosis and mode identification are extremely vital to maintain the system safe and reliable. Recently, many methods have been proposed to address these problems separately. Moreover, many of them make an assumption that the data from industrial site only contain the Gaussian noise. However, this assumption is not held in practice, which further reduces their performances. Considering the complicated noise feature of industrial data, we came up with an improved dictionary learning method to settle these problems simultaneously. First, the measurement data were decomposed into three parts: clean data, mode-based noise, and dense Gaussian noise. Then, the dictionary learning method was proposed to characterize each part separately. Inspired by the framework of label-consistent K-SVD, the mode information was incorporated into the dictionary learning method, and we developed a solution to settle the multiple dictionary learning optimization problem. Finally, when new samples arrive, we reconstruct them under the learned dictionary so that each sample's mode and abnormal data and can be determined. The experiments on two different types of simulated process and aluminum electrolysis process show the strength and reliability of our method, which indicates the engineering application value of the proposed method.

Energy Efficient and Low-Cost Server Architecture for Hadoop Storage Appliance

This paper proposes the Lempel-Ziv 4(LZ4) compression accelerator optimized for scale-out servers in data centers. In order to reduce CPU loads caused by compression, we propose an accelerator solution and implement the accelerator on an Field Programmable Gate Array(FPGA) as heterogeneous computing. The LZ4 compression hardware accelerator is a fully pipelined architecture and applies 16 dictionaries to enhance the parallelism for high throughput compressor. Our hardware accelerator is based on the 20-stage pipeline and dictionary architecture, highly customized to LZ4 compression algorithm and parallel hardware implementation. Proposing dictionary architecture allows achieving high throughput by comparing input sequences in multiple dictionaries simultaneously compared to a single dictionary. The experimental results provide the high throughput with intensively optimized in the FPGA. Additionally, we compare our implementation to CPU implementation results of LZ4 to provide insights on FPGA-based data centers. The proposed accelerator achieves the compression throughput of 639MB/s with fine parallelism to be deployed into scale-out servers. This approach enables the low power Intel Atom processor to realize the Hadoop storage along with the compression accelerator.

Use of Social Networks in Determining stockmarket Evolution

Abstract This article aims to use text mining methods and sentiment analysis to determine the stock market evolution of companies as well as virtual currencies such as Bitcoin. The source of the text is the social media channel Twitter and the text is composed of individual messages sent by users. Although previous papers proved with a degree of certainty that this paper hypothesis is true, as we will see bellow, the area of research was focused only on the professional environment or known opinion makers and not taking into account a high population mass. To ensure that a high level of information is maintained after the sentiment analysis process, we will use multiple algorithms based on different calculation methods and different word dictionaries. In addition, indicators such as the number of assessments, the number of replays etc. will be added to the methodology. By the end of the paper we will be able to both identify a working methodology of analyzing text for the purposes of stock market prediction and also we will touch on the limitations faced when creating it and the ways through which we can expand and improve it’s reliability. The implementation of all these methods and of the multiple dictionaries helped us in simulating human behavior and the differences of opinion, when a group wants to analyze a text. The algorithm becoming a way to balance the different “opinions” that resulted out of the sentiment analysis.

Automatic and Accurate Expansion of Abbreviations in Parameters

Abbreviations are widely used in identifiers. However, they have severe negative impact on program comprehension and IR-based software maintenance activities, e.g., concept location, software clustering, and recovery of traceability links. Consequently, a number of efficient approaches have been proposed successfully to expand abbreviations in identifiers. Most of such approaches rely heavily on dictionaries, and rarely exploit the specific and fine-grained context of identifiers. As a result, such approaches are less accurate in expanding abbreviations (especially short ones) that may match multiple dictionary words. To this end, in this paper we propose an automatic approach to improve the accuracy of abbreviation expansion by exploiting the specific and fine-grained context. It focuses on a special but common category of abbreviations (abbreviations in parameter names), and thus it can exploit the specific and fine-grained context, i.e., the type of the enclosing parameter as well the corresponding formal (or actual) parameter name. The recent empirical study on parameters suggest that actual parameters are often lexically similar to their corresponding formal parameters. Consequently, it is likely that an abbreviation in a formal parameter can find its full terms in the corresponding actual parameter, and vice versa. Based on this assumption, a series of heuristics are proposed to look for full terms from the corresponding actual (or formal) parameter names. To the best of our knowledge, we are the first to expand abbreviations by exploiting the lexical similarity between actual and formal parameters. We also search for full terms in the data type of the enclosing parameter. Only if all such heuristics fail, the approach turns to the traditional abbreviation dictionaries. We evaluate the proposed approach on seven well known open-source projects. Evaluation results suggest that when only parameter abbreviations are involved, the proposed approach can improve the precision from 26 to 95 percent and recall from 26 to 65 percent compared against the state-of-the-art general purpose approach. Consequently, the proposed approach could be employed as a useful supplement to existing approaches to expand parameter abbreviations.

E-Dictionaries in a Network of Information Tools in the e-Environment

Traditional dictionaries offer curated data to users. Users should therefore be able to find the correct data to solve their information need. However, users don't necessarily know the exact scope of lexicographic information. Dictionary articles can still demand considerable interpretation by the user to select the appropriate meaning or equivalent. In the e-environment, users can easily navigate between different e-sources. This is especially evident on various e-book platforms, where one can link multiple dictionaries and other sources to a text or search of the internet. Internet content is obviously not curated, and providing access to such data is therefore anathema to the traditional lexicographer. A traditional dictionary is the result of an application of data pushing procedures. The online environment enables the use of data pulling procedures that give users access to both curated and non-curated data. These issues are illustrated by means of a number of examples that show that a large number of different and disparate information sources are easily available to the user to satisfy any specific information need, and that the dictionary is one of a plethora of information sources. The information is therefore available on demand, without risking information overload. It is argued that, when optimising a network of information tools that constitutes a comprehensive search universe, the information retrieval structure should preferably have a circular network as application domain, rather than a linear continuum.

Learning Category-Specific Sharable and Exemplary Visual Elements for Image Classification

This paper presents a novel method to determine the discriminative image representation via visual dictionary learning framework for image classification task. Visual dictionary learning has the capacity to represent input image using an over-complete element set. Sparsity restrains distractors and prevents over-fitting. The two main characteristics benefit the classification solution. However, one shortcoming of existing dictionary learning is that it neglects to exploit the potential correlations across visual elements, especially from the category-specific feature space. To address this problem, we first propose to learn multiple discriminative category-specific dictionaries (DCSD) from all categories. The DCSD can explore the visual elements from each category in terms of sharable property. For this reason, these learned category-specific visual elements encourage image features from the same class to have the similar feature representations. In addition, exemplary data reflect the main characteristic of whole dataset and can improve the performance of algorithm that employs them. Therefore, we further propose a representative pattern dictionary (RPD) model to discover the exemplary visual elements for promoting the discriminative capability of feature representation. These exemplary visual elements are essentially a subset of over-complete visual elements and can represent the whole sample data effectively. Finally, we design a novel strategy that integrates the merits of object proposals and deep features jointly to strengthen the semantic information of image-level feature. Experimental results on benchmark datasets demonstrate the effectiveness of our method, which is shown to be superior to the recently competing dictionary learning and deep learning based image classification approaches.

Hierarchical Discriminative Deep Dictionary Learning

In deep dictionary learning multiple dictionaries are learned based on information at various levels of abstraction. We propose a novel hierarchical discriminative dictionary learning layer embedded within a neural network with an image classification objective. Discrimination is induced in the learned synthesis dictionaries at multiple hierarchical levels in a simple way by a one-hot-code representation of the class labels during the training backward pass. In addition, local sparse representation objectives are approximated during the forward pass, introducing local regularization. We evaluate our proposal on five known datasets and we either outperform state-of-the-art methods or achieve competitive classification results.

Genomic Sequence Data Compression using Lempel Ziv Welch Algorithm with Indexed Multiple Dictionary

With the advancement in technology and development of High Throughput System (HTS), the amount of genomic data generated per day per laboratory across the globe is surpassing the Moore’s law. The huge amount of data generated is of concern to the biologists with respect to their storage as well as transmission across different locations for further analysis. Compression of the genomic data is the wise option to overcome the problems arising from the data deluge. This paper discusses various algorithms that exists for compression of genomic data as well as a few general purpose algorithms and proposes a LZW-based compression algorithm that uses indexed multiple dictionaries for compression. The proposed method exhibits an average compression ratio of 0.41 bits per base and an average compression time of 6.45 secs for a DNA sequence of an average size 105.9 KB.

Transient extraction based on minimax concave regularized sparse representation for gear fault diagnosis

Extraction of fault-induced transients is one of the difficulties in gear fault diagnosis as the vibration signal is composed of multiple components. Sparse representation is one of the effective methods for extracting transients from such noisy and multicomponent vibration signals. However, conventional sparse representation approaches have the problems of amplitude underestimation, computational inefficiency and mono-component orientation. To address such problems, this paper first proposes to use minimax concave penalty function to construct the objective function as the minimax concave penalty function outperforms other penalty functions in preserving high-amplitude components based on the comparisons among different non-convex penalty functions. Then, the multiple over-complete dictionaries are established based on Fourier bases, where every two elements of each dictionary form a tight frame, enabling to simplify calculation and avoid computing high-dimensional inverse matrix when performing iterative optimization. Since multiple dictionaries are constructed, the multi-components of gear vibration signal can be extracted, respectively. With the constructed dictionaries, the sparse representation coefficients can be calculated using split augmented Lagrangian shrinkage algorithm and the repetitive transients can then be extracted. The simulation study and experimental analysis show that the fault transients and harmonic components can be accurately as well as computational-effectively extracted without underestimation of high amplitude components. Comparison results between minimax concave, arctan and L1 norm also prove the superior high-amplitude preserving ability of the proposed method.

Incremental learning-based visual tracking with weighted discriminative dictionaries

Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out.

Multi-Direction Dictionary Learning Based Depth Map Super-Resolution With Autoregressive Modeling

3D depth cameras have become more and more popular in recent years. However, depth maps captured by these cameras can hardly be used in 3D reconstruction directly because they often suffer from low resolution and blurring depth discontinuities. Super resolution of depth maps is necessary. In depth maps, the edge areas play more important role and demonstrate distinct geometry directions compared with natural images. However, most existing super-resolution methods ignore this fact, and they can not handle depth edges properly. Motivated by this, we propose a compound method that combines multi-direction dictionary sparse representation and autoregressive (AR) models, so that the depth edges are presented precisely at different levels. In the patch level, the depth edge patches with geometry directions are well represented by the pre-trained multi-directional dictionaries. Compared with a universal dictionary, multiple dictionaries trained from different directional patches can represent the directional depth patch much better. In the finer pixel level, we utilize an adaptive AR model to represent the local correlation patterns in small areas. Extensive experimental results on both synthetic and real datasets demonstrate that, the proposed model outperforms state-of-the-art depth map super-resolution methods in terms of both quantitative metrics and subjective visual quality.

Efficient VLSI architecture for the parallel dictionary LZW data compression algorithm

The Lempel–Ziv–Welch (LZW) algorithm is an important dictionary-based data compression approach that is used in many communication and storage systems. The parallel dictionary LZW (PDLZW) algorithm speeds up the LZW encoding by using multiple dictionaries. This simplifies the parallel search in the dictionaries. However, the compression gain of the PDLZW depends on the partitioning of the address space, i.e. on the sizes of the parallel dictionaries. This work proposes an address space partitioning technique that optimises the compression rate of the PDLZW. Numerical results for address spaces with 512, 1024, and 2048 entries demonstrate that the proposed address partitioning improves the performance of the PDLZW compared with the original proposal. These address space sizes are suitable for flash storage systems. Moreover, the PDLZW has relative high memory requirements which dominate the costs of a hardware implementation. This work proposes a recursive dictionary structure and a word partitioning technique that significantly reduce the memory size of the parallel dictionaries.