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Objective: Carotid ultrasonography is a reliable and non-invasive method to evaluate atherosclerosis disease and its complications. B-mode cineloops are widely used to assess the severity of atherosclerosis and its progression; ho- wever, tracking rapid wall motions of the carotid artery is still a challenging issue due the low frame rate. The aim of this paper was to present a new hybrid frame rate up-conversion (FRUC) method that accounts for motion based on manifold learning and optical flow. Methods: In the last decade, manifold learning technique has been used to pseudo-increase the frame rate of carotid ultrasound images, but due to the dependence of this method to the number of recorded cardiac cycles and frames, a new hybrid method based on manifold learning and optical flow was proposed in this paper. Results: Locally linear embedding (LLE) algorithm was first applied to find the relation between the frames of consecutive cardiac cycles in a low dimensional manifold. Then by applying the optical flow motion estimation algorithm, a motion compensated frame was reconstructed. Conclusion: Consequently, a cycle with more frames was created to provide a more accurate consideration of carotid wall motion compared to the typical B-mode ultrasound ima-ges. The results revealed that our new hybrid method outperforms the pseudo-increasing frame rate scheme based on manifold learning.

In the era of healthcare, and its related research fields, the dimensionality problem of high dimensional data is a massive challenge as it contains a huge number of variables forming complex data matrices. The demand for dimension reduction of complex data is growing immensely to improvise data prediction, analysis and visualization. In general, dimension reduction techniques are defined as a compression of dataset from higher dimensional matrix to lower dimensional matrix. Several computational techniques have been implemented for data dimension reduction, which is further segregated into two categories such as feature extraction and feature selection. In this review, a detailed investigation of various feature extraction and feature selection methods has been carried out with a systematic comparison of several dimension reduction techniques for the analysis of high dimensional data and to overcome the problem of data loss. Then, some case studies are also cited to verify the better approach for data dimension reduction by considering few advances described in the technical literature. This review paper may guide researchers to choose the most effective method for satisfactory analysis of high dimensional data.

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

Since its introduction in 2000, Locally Linear Embedding (LLE) has been widely applied in data science. We provide an asymptotical analysis of LLE under the manifold setup. We show that for a general manifold, asymptotically we may not obtain the Laplace–Beltrami operator, and the result may depend on nonuniform sampling unless a correct regularization is chosen.We also derive the corresponding kernel function, which indicates that LLE is not a Markov process. A comparison with other commonly applied nonlinear algorithms, particularly a diffusion map, is provided and its relationship with locally linear regression is also discussed.

This paper considers the problem of effective feature extraction of acoustic signals from oil and gas pipelines under different working conditions. A feature extraction of pipeline leakage detection method is proposed based on multi-feature entropy fusion and local linear embedding (LLE). First, seven kinds of commonly used entropy which can reflect the characteristics of the signal better are extracted from the pipeline signal through experiments, including permutation entropy, envelope entropy, approximate entropy, fuzzy entropy, energy entropy, sample entropy and dispersion entropy. The seven-dimensional feature vectors are obtained by feature fusion. Second, the LLE algorithm is used to reduce the dimension of the feature vector to complete the secondary feature extraction. Finally, the support vector machine (SVM) is used to identify the working conditions of the pipeline. The experimental results show that, compared with other dimensionality reduction methods, single-feature entropy method and multi-feature entropy fusion method, the proposed method can identify the types of pipeline working conditions effectively and reduce the problems of false negatives and false positives in pipeline leakage detection.

Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer's disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of local linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifier for predicting future conversion to AD based on a baseline MRI. We tested the approach on 413 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had baseline MRI scans and complete clinical follow-ups over 3years with the following diagnoses: cognitive normal (CN; n=137), stable mild cognitive impairment (s-MCI; n=93), MCI converters to AD (c-MCI, n=97), and AD (n=86). We found that classifications using embedded MRI features generally outperformed (p<0.05) classifications using the original features directly. Moreover, the improvement from LLE was not limited to a particular classifier but worked equally well for regularized logistic regressions, support vector machines, and linear discriminant analysis. Most strikingly, using LLE significantly improved (p=0.007) predictions of MCI subjects who converted to AD and those who remained stable (accuracy/sensitivity/specificity: =0.68/0.80/0.56). In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: =0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD. •Locally linear embedding (LLE) is an unsupervised learning algorithm.•It was used to extract characteristic MR features of brain alternations.•It was used to classify normal aging subjects, MCI and AD patients from ADNI data.•The performance of predicting AD in MCIs was significantly improved by using LLE.•LLE benefitted various classifiers, such as SVM, LDA and regularized regressions.

Black soil in northeast China is gradually degraded and soil organic matter (SOM) content decreases at a rate of 0.5% per year because of the long-term cultivation. SOM content can be obtained rapidly by visible and near-infrared (Vis–NIR) spectroscopy. It is critical to select appropriate preprocessing techniques for SOM content estimation through Vis–NIR spectroscopy. This study explored three categories of preprocessing techniques to improve the accuracy of SOM content estimation in black soil area, and a total of 496 ground samples were collected from the typical black soil area at 0–15 cm in Hai Lun City, Heilongjiang Province, northeast of China. Three categories of preprocessing include denoising, data transformation and dimensionality reduction. For denoising, Svitzky-Golay filter (SGF), wavelet packet transform (WPT), multiplicative scatter correction (MSC), and none (N) were applied to spectrum of ground samples. For data transformation, fractional derivatives were allowed to vary from 0 to 2 with an increment of 0.2 at each step. For dimensionality reduction, multidimensional scaling (MDS) and locally linear embedding (LLE) were introduced and compared with principal component analysis (PCA), which was commonly used for dimensionality reduction of soil spectrum. After spectral pretreatments, a total of 132 partial least squares regression (PLSR) models were constructed for SOM content estimation. Results showed that SGF performed better than the other three denoising methods. Low-order derivatives can accentuate spectral features of soil for SOM content estimation; as the order increases from 0.8, the spectrum were more susceptible to spectral noise interferences. In most cases, 0.2–0.8 order derivatives exhibited the best estimation performance. Furthermore, PCA yielded the optimal predictability, the mean residual predictive deviation (RPD) and maximum RPD of the models using PCA were 1.79 and 2.60, respectively. The application of appropriate preprocessing techniques could improve the efficiency and accuracy of SOM content estimation, which is important for the protection of ecological and agricultural environment in black soil area.

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.

How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key, which is usually solved by reducing dimensionality. Principle component analysis (PCA) is the most widely-used linear dimensionality reduction method at present. However, the relationships between seismic attributes and reservoir features are non-linear, so seismic attribute dimensionality reduction based on linear transforms can’t solve non-linear problems well, reducing reservoir prediction precision. As a new non-linear learning method, manifold learning supplies a new method for seismic attribute analysis. It can discover the intrinsic features and rules hidden in the data by computing low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. In this paper, we try to extract seismic attributes using locally linear embedding (LLE), realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters. Combining model analysis and case studies, we compare the dimensionality reduction and clustering effects of LLE and PCA, both of which indicate that LLE can retain the intrinsic structure of the inputs. The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies, reservoir, and even reservoir fluids.

Plantar heel pain mainly manifests during the gait cycle when the whole foot is in contact with the floor, which corresponds to the second rocker of the gait. This moment can be studied through the analysis of pressure–time graphs obtained using plantar pressure plate systems. However, these graphs are complex, and a dimensionality reduction method, such as locally linear embedding (LLE), greatly assists in their comprehension. This observational, case-control pilot study included 45 subjects divided into case (n = 21) and control (n = 24) groups, depending on the presence/absence of plantar heel pain. The second rocker pressure–time graphs of the 45 subjects were obtained using the Footwork Pro® plantar pressure plate system. These graphs were analyzed and defined as the dynamic simultaneity surfaces (DSSs). This complex structure was composed of four dimensions: the dynamic simultaneity time (DST), slope upward grade (α), slope downward grade (β), and height (h), and were reduced into one dimension and classified into pathological and non-pathological subjects using the LLE method. All 45 DSSs were successfully reduced and classified to distinguish between the case (plantar heel pain) and control (non-plantar heel pain) subjects. This study is the first to use the LLE method for gait analysis. This method serves as a novel and promising tool for the study and classification of pathological and non-pathological gait cycles. This method opens the door for future research and analysis, with significant potential to assess diagnosis, treatment follow-up, and injury prevention in physical medicine consultations.