Sparse Prototypes Research Articles

Improvement of an Industrial Robotic Flaw Detection System

Rotary encoders are commonly used for dynamic control and positioning of industrial robots. Results of this study suggested that rotary encoder signal can also be used to monitor the efficiency of industrial robot systems effectively after proper processing. A novel strategy using singular spectrum analysis (SSA) integrated with hierarchical hyper-Laplacian prior prototype (HHLP) is proposed in this study for defect detection in industrial robots. Sparsity-assisted techniques are efficient flaw-based extraction techniques that have been extensively investigated in recent years. However, the selection of an appropriate sparse prior from the point of view of probability theory remains unverified. First, SSA enables the separation of complicated encoding signals to several interpretable components, including a trend, a set of cyclic oscillations, and residual oscillations (noise). Second, we describe HHLP by maximizing posterior probability of robot flaw diagnosis. HHLP is proposed to extract noise interference, cyclic pulse, and harmonic interference from the residual signal using the SSA technique. We infer that the hyper-Laplacian prior in the prototype may present a more efficient prototype flaw than the Laplacian prior. In addition, HHLP incorporates physical characteristics necessary to distinguish harmonic intervention. This study primarily establishes a modern prototype that represents the former dispersed from the perspective of maximizing the probability of the latter. Meanwhile, generalized minimax-concave regularization inductive and kurtosis-based weighted sparse prototypes are compared and spectral kurtosis is used to confirm the efficacy of HHLP. Note to Practitioners—This study aims to solve the problem of industrial robot fault diagnosis during the operation process to avoid production delays. Rotary encoder sensor is attached to each joint to collect raw information and identify the robot position. Data from the rotary encoder sensor can also be used for the efficient health status assessment of the performance of the industrial robot system after proper processing. Therefore, a new approach using singular spectrum analysis combined with hierarchical hyper-Laplacian pre-induced prototype is proposed in this study. The residual signal extracted using the singular spectrum analysis method for processing in a hierarchical hyper-Laplacian pre-induced prototype is used to improve the weak fault feature. We describe a hierarchical hyper-Laplacian preprototype by maximizing the posterior probability of the flaw diagnosis. We introduce a hierarchical hyper-Laplacian prior that integrates physical characteristics to distinguish between harmonic interferences. The distribution of coefficients acquired through other dictionaries or transformations and selection of the optimal priority for the extraction of flaw characteristics will be the foci of future investigations.

Mixture of von Mises-Fisher distribution with sparse prototypes

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation–maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model’s behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.

Terahertz Band-Pass Filters for Wideband Superconducting On-Chip Filter-Bank Spectrometers

A superconducting microstrip half-wavelength resonator is proposed as a suitable band-pass filter for broadband moderate spectral resolution spectroscopy for terahertz (THz) astronomy. The proposed filter geometry has a free spectral range of an octave of bandwidth without introducing spurious resonances, reaches a high coupling efficiency in the pass-band and shows very high rejection in the stop-band to minimize reflections and cross-talk with other filters. A spectrally sparse prototype filter-bank in the band 300-400 GHz has been developed employing these filters as well as an equivalent circuit model to anticipate systematic errors. The fabricated chip has been characterized in terms of frequency response, reporting an average peak coupling efficiency of 27% with an average spectral resolution of 940.

Reliability growth planning based on information gap decision theory

Abstract Resources allocation is one of the key issues in the planning of reliability growth testing. Sparse information and testing prototypes are available for program managers in the research and development phases, especially at the early development stage. However, program managers or engineers are often needed to make decisions with incomplete information or even severe uncertainty. Optimizing strategy has been used to allocate resources for reliability growth testing, which sets up optimization models and maximizes the reliability within the resource constraints. A novel robust decision method for the planning of reliability growth testing based on the information gap decision theory is proposed, which aims to satisfy the system reliability requirements and keeps the decision insensitive to the initial estimation of relevant uncertain parameters. The information gap robustness function provides an alternative approach to address the planning of reliability growth testing. The case study demonstrates the applicability of the proposed method in practical problems. The main advantage of this method is that only a few information of the uncertain parameters is required. The results indicate that this method is useful for program managers and reliability practitioners who are engaged in reliability growth planning.

Adaptation of person re-identification models for on-boarding new camera(s)

Existing approaches for person re-identification have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where one or multiple new cameras may be temporarily on-boarded into an existing system to get additional information or added to expand an existing network. To address such a very practical problem, we propose a novel approach for adapting existing multi-camera re-identification frameworks with limited supervision. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with newly introduced target camera(s), without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Third, we develop a target-aware sparse prototype selection strategy for finding an informative subset of source camera data for data-efficient learning in resource constrained environments. Our approach can greatly increase the flexibility and reduce the deployment cost of new cameras in many real-world dynamic camera networks. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art unsupervised alternatives whilst being extremely efficient to compute.

Incremental online object tracking via superpixel dimension reduction

Visual object tracking based on the principal component analysis (PCA) has been widely used in the last few decades as it deals with appearance changes caused by in-plane rotation, out-of-plane rotation, illumination variation and motion blur. But this method fails to handle partial occlusion problem. Also, it has computational complexity and speed problem when the size of image resolution is large. In this paper, a new method is proposed based on superpixel, PCA and sparse prototypes. The size of the image is reduced with preserving much of the visual information by superpixel segmentation. Then, the incremental PCA subspace representation is applied to model target appearance and account of occlusion with trivial templates. Both qualitative and quantitative analyses are done in many challenging image sequences. The proposed method gives a better performance than that of the other state-of-the-art algorithms.

Saliency Detection with Sparse Prototypes: An Approach Based on Multi-Dictionary Sparse Encoding

This paper proposes a bottom-up saliency detection algorithm based on multi-dictionary sparse recovery. Firstly, the SLIC algorithm is used to segment the image into superpixels in multilevel and atoms with a high background possibility are selected from the boundary superpixels to construct the multidictionary. Secondly, sparse recovery of the entire image is achieved using multi-dictionary to get subsaliency maps from the perspective of sparse recovery errors. The final saliency map is generated in a weighted fusion manner. Experimental results on three public datasets demonstrate the effectiveness of our model.

Visual tracking via robust multitask sparse prototypes

Sparse representation has been applied to an online subspace learning-based tracking problem. To handle partial occlusion effectively, some researchers introduce l1 regularization to principal component analysis (PCA) reconstruction. However, in these traditional tracking methods, the representation of each object observation is often viewed as an individual task so the inter-relationship between PCA basis vectors is ignored. We propose a new online visual tracking algorithm with multitask sparse prototypes, which combines multitask sparse learning with PCA-based subspace representation. We first extend a visual tracking algorithm with sparse prototypes in multitask learning framework to mine inter-relations between subtasks. Then, to avoid the problem that enforcing all subtasks to share the same structure may result in degraded tracking results, we impose group sparse constraints on the coefficients of PCA basis vectors and element-wise sparse constraints on the error coefficients, respectively. Finally, we show that the proposed optimization problem can be effectively solved using the accelerated proximal gradient method with the fast convergence. Experimental results compared with the state-of-the-art tracking methods demonstrate that the proposed algorithm achieves favorable performance when the object undergoes partial occlusion, motion blur, and illumination changes.

Scaling up graph-based semisupervised learning via prototype vector machines.

When the amount of labeled data are limited, semisupervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via l1 -regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning.

Robust embedded vision system for face detection and identification in smart surveillance

Over recent years, the demand for face video analytics in surveillance cameras is increasing dramatically due to the need for real-time event detection in commercial and security applications. The main objective of this paper is to implement robust face detection and identification system on a programmable resource constrained digital video processor. In order to reduce the unnecessary computations involved in Viola and Jones (V-J) face detector, we have proposed skin detection based search window reduction technique. After that, to identify the detected faces, we have proposed sparse prototypes based face identification algorithm which can address partial occlusion and expression variation issues effectively. Furthermore, within the face identification framework, a simple rejection ratio has been proposed to reject the invalid test faces. Experimental results of the developed embedded vision system demonstrate that the proposed methods can achieve satisfactory performance in uncontrolled settings.

Adaptive conformal semi-supervised vector quantization for dissimilarity data

Existing semi-supervised learning algorithms focus on vectorial data given in Euclidean space. But many real life data are non-metric, given as (dis-)similarities which are not widely addressed. We propose a conformal prototype-based classifier for dissimilarity data to semi-supervised tasks. A ‘secure region’ of unlabeled data is identified to improve the trained model based on labeled data and to adapt the model complexity. The new approach (i) can directly deal with arbitrary symmetric dissimilarity matrices, (ii) offers intuitive classification by sparse prototypes, (iii) adapts the model complexity. Experiments confirm the effectiveness of our approach in comparison to state-of-the-art methods.

Correction: Class Prediction and Feature Selection with Linear Optimization for Metagenomic Count Data

A reader recently pointed out certain similarities between the text in the article and that from some previous publications. Although our method in PLOS ONE was novel and citations to many of the relevant publications were included, the text should not have been used verbatim. The authors would like to thank the reader and apologize for the overlap in text. The overlap in the text relates to the Introduction, Methods and Discussion sections of the article, where sentences from previous publications were reproduced, this relates to the following fragments in the text: In the Methods section of the article there is some overlap in text with the first author Zhenqiu Liu’s previous publication, cited as reference 32: Liu Z, Hsiao W, Cantarel B, Drbek E, Fraser-Liggett C (2011). Sparse distance based learning for simultaneous multiclass classification and feature selection of metagenomic data. Bioinformatics 27(23): 3242–3249. The study reported in Bioinformatics and that published in PLOS ONE studied the same multiclass classification problem for metagenomic count data, and thus mathematical notations and the problem are defined in a similar way in the two articles. In addition, the description of the data normalization procedures in the PLOS ONE article is given in a way similar to that in the earlier publication which in Bioinformatics. The description of the methods that overlap with that in the publication in Bioinformatics relates to the first paragraphs of the Methods section before the section titled and Penalized SVM Methods. With regard to equations (6) and (7), our article cited the following article by Quattroni et al. as reference 35: Quattoni A, Carreras X, Collins M, and Darrell T (2009). An Efficient Projection for L1,∞ Regularization, ICML 2009 However we acknowledge that it would have been more appropriate to cite this earlier work by the same group: Quattoni A, Collins M, and Darrell T (2008), Transfer Learning for Image Classification with Sparse Prototype Representations. In Proceedings of CVPR 2008. Other instances of text overlap occur in the Introduction and in the Discussion sections of the article. In the first paragraph of the Introduction, there are sentences that overlap with text in references 4 and 7, as well as Schloss et al. BMC Bioinformatics. 2008 Jan 23;9:34. doi: 10.1186/1471-2105-9-34, which was not cited in the article: ‘The majority of microbes reside in the gut, have a profound influence on human physiology and nutrition, and are crucial for human life. Metagenomics, the culture-independent isolation and characterization of DNA from uncultured microorganisms, has facilitated the analysis of the functional biodiversity harbored in the large reservoir of uncultured bacteria and archaea.’ ‘Recent advances in genome sequencing technologies have made obtaining a complete metagenomic sequencing more tractable. Having on hand such a large number of microbial genomes has changed the nature of microbiology and of microbial evolution studies. By providing the ability to examine the relationship of genome structure and function across many different species, these data have also opened up the fields of comparative genomics and of systems biology. A main promise of metagenomics is that it will accelerate drug discovery and biotechnology by providing new genes with novel functions’ In the Results section, there are sentences that overlap with text from references 26 and 38 in the article: ‘ are simulated from the Gamma distribution with a mean () of 100 and variance () of 1000. We simulated 1000 features for each sample from NB distributions, which contained the first 5 relevant features having different distributions with distinguished s. We used two-fold cross validation to evaluate the method. First, we normalized the data with proportion and arcsin transformations, and then divided the data into training and test equal subsets. The training subset was used for model construction, while the test subset was used to evaluate performance. The model parameters are determined from only the training data with leave-one-out cross-validation.’ ‘Bacteria thrive on and within the human body. One of the largest human-associated microbial habitats is the skin surface, which harbors large numbers of bacteria that can have important effects on health.’ Even though we cited the articles above, we apologize that the information was not rewritten more carefully. The issue of existing text overlap has no bearing on the results and conclusions of the study.

Boosting k-NN for Categorization of Natural Scenes

The k-nearest neighbors (k-NN) classification rule has proven extremely successful in countless many computer vision applications. For example, image categorization often relies on uniform voting among the nearest prototypes in the space of descriptors. In spite of its good generalization properties and its natural extension to multi-class problems, the classic k-NN rule suffers from high variance when dealing with sparse prototype datasets in high dimensions. A few techniques have been proposed in order to improve k-NN classification, which rely on either deforming the nearest neighborhood relationship by learning a distance function or modifying the input space by means of subspace selection. From the computational standpoint, many methods have been proposed for speeding up nearest neighbor retrieval, both for multidimensional vector spaces and nonvector spaces induced by computationally expensive distance measures. In this paper, we propose a novel boosting approach for generalizing the k-NN rule, by providing a new k-NN boosting algorithm, called UNN (Universal Nearest Neighbors), for the induction of leveraged k-NN. We emphasize that UNN is a formal boosting algorithm in the original boosting terminology. Our approach consists in redefining the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. Therefore, the k nearest neighbors examples act as weak classifiers and their weights, called leveraging coefficients, are learned by UNN so as to minimize a surrogate risk, which upper bounds the empirical misclassification rate over training data. These leveraging coefficients allows us to distinguish the most relevant prototypes for a given class. Indeed, UNN does not affect the k-nearest neighborhood relationship, but rather acts on top of k-NN search. We carried out experiments comparing UNN to k-NN, support vector machines (SVM) and AdaBoost on categorization of natural scenes, using state-of-the art image descriptors (Gist and Bag-of-Features) on real images from Oliva and Torralba (Int. J. Comput. Vis. 42(3):145–175, 2001), Fei-Fei and Perona (IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 524–531, 2005), and Xiao et al. (IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3485–3492, 2010). Results display the ability of UNN to compete with or beat the other contenders, while achieving comparatively small training and testing times.

Online Object Tracking With Sparse Prototypes

Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking algorithm with sparse prototypes, which exploits both classic principal component analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance models. We introduce l(1) regularization into the PCA reconstruction, and develop a novel algorithm to represent an object by sparse prototypes that account explicitly for data and noise. For tracking, objects are represented by the sparse prototypes learned online with update. In order to reduce tracking drift, we present a method that takes occlusion and motion blur into account rather than simply includes image observations for model update. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Text-Based On-Line Conferencing: A Conceptual and Empirical Analysis Using a Minimal Prototype

This article is concerned with an analysis of the requirements for text-based on-line conferencing. From a system perspective, text-based on-line conferencing can be viewed as either message passing or data sharing. These complementary views give rise to different design dimensions. For example, the message-passing view is concerned with granularity, channels, message labels, and so on. The data-sharing view is concerned with the access different individuals have to the text: read only, appending, editing, pointing, and so on. A deliberately sparse prototype was built and placed in this design space. This minimal prototype has limited functionality so that the real problems experienced by users can show through. Relevant literature from disciplines such as social psychology, conversational analysis, and linguistics is briefly reviewed in terms of three generic communication tasks: synchronizing communication, maintaining structural coherence, and maintaining referents. An empirical analysis of subjects' use of the sparse prototype was analyzed to establish the relevance of the generic communication tasks to text-based on-line conferencing. Possible forms that support for these tasks might take are discussed.