Locally linear embedding-based seismic attribute extraction and applications

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.

Fault Detection Based on Incremental Locally Linear Embedding for Satellite TX-I

A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental learning,an incremental LLE method is proposed to acquire low-dimensional feature embedded in high-dimensional space.Then,telemetry data of Satellite TX-I are analyzed.Therefore,fault detection are performed by analyzing feature information extracted from the telemetry data with the statistical indexes T2 and squared prediction error(SPE)and SPE.Simulation results verify the fault detection scheme.

Nonlinear fault detection based on locally linear embedding

In this paper, a new nonlinear fault detection technique based on locally linear embedding （LLE） is developed. LLE can efficiently compute the low-dimensional embedding of the data with the local neighborhood structure information preserved. In this method, a data-dependent kernel matrix which can reflect the nonlinear data structure is defined. Based on the kernel matrix, the Nystrrm formula makes the mapping extended to the testing data possible. With the kernel view of the LLE, two monitoring statistics are constructed. Together with the out of sample extensions, LLE is used for nonlinear fault detection. Simulation cases were studied to demonstrate the performance of the proposed method.

Recognition algorithm for plant leaves based on adaptive supervised locally linear embedding

Locally linear embedding(LLE)algorithm has a distinct deficiency in practical application.It requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is presented based on supervised LLE in this article.A similarity measure is formed by utilizing the Fisher projection distance,and then it is used as a threshold to select k.Different samples will produce different k adaptively according to the density of the data distribution.The method is applied to classify plant leaves.The experimental results show that the average classification rate of this new method is up to 92.4%,which is much better than the results from the traditional LLE and supervised LLE.

Nonlinearly correlated failure analysis and autonomic prediction for distributed systems

In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.

LLE-BASED CLASSIFICATION ALGORITHM FOR MMW RADAR TARGET RECOGNITION

In this paper,a new multiclass classification algorithm is proposed based on the idea of Locally Linear Embedding(LLE),to avoid the defect of traditional manifold learning algorithms,which can not deal with new sample points.The algorithm defines an error as a criterion by computing a sample's reconstruction weight using LLE.Furthermore,the existence and characteristics of low dimensional manifold in range-profile time-frequency information are explored using manifold learning algorithm,aiming at the problem of target recognition about high range resolution MilliMeter-Wave(MMW) radar.The new algorithm is applied to radar target recognition.The experiment results show the algorithm is efficient.Compared with other classification algorithms,our method improves the recognition precision and the result is not sensitive to input parameters.

Fast color transfer from multiple images

Color transfer between images uses the statistics information of image effectively. We present a novel approach of local color transfer between images based on the simple statistics and locally linear embedding. A sketching interface is proposed for quickly and easily specifying the color correspondences between target and source image. The user can specify the corre- spondences of local region using scribes, which more accurately transfers the target color to the source image while smoothly preserving the boundaries, and exhibits more natural output results. Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different regions in the source image. Moreover, our algorithm does not require to choose the same color style and image size between source and target images. We propose the sub-sampling to reduce the computational load. Comparing with other approaches, our algorithm is much better in color blending in the input data. Our approach preserves the other color details in the source image. Various experimental results show that our approach specifies the correspondences of local color region in source and target images. And it expresses the intention of users and generates more actual and natural results of visual effect.

Image feature optimization based on nonlinear dimensionality reduction

Image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping between highand low-dimensional space via a five-tuple model. Nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding (GNLLE) algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE) algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.

Autonomic failure prediction based on manifold learning for large-scale distributed systems

This article investigates autonomic failure prediction in large-scale distributed systems with nonlinear dimensionality reduction to automatically extract failure features. Most existing methods for failure prediction focus on building prediction models or heuristic rules by discovering failure patterns, but the process of feature extraction before failure patterns recognition is rarely considered due to the increasing complexity of modern distributed systems. In this work, a novel performance-centric approach to automate failure prediction is proposed based on manifold learning （ML）. In addition, the ML algorithm named supervised locally linear embedding （SLLE） is applied to achieve feature extraction. To generalize the dimensionality reduction mapping, the nonlinear mapping approximation and optimization solution is also proposed. In experimental work a file transfer test bed with fault injection is developed which can gather multilevel performance metrics transparently. Based on the runtime monitoring of these metrics, the SLLE method can automatically predict more than 50% of the central processing unit （CPU） and memory failures, and around 70% of the network failure.

Facial Expression Recognition of Various Internal States via Manifold Learning

Emotions are becoming increasingly important in human-centered interaction architectures. Recognition of facial expressions, which are central to human-computer interactions, seems natural and desirable. However, facial expressions include mixed emotions, continuous rather than discrete, which vary from moment to moment. This paper represents a novel method of recognizing facial expressions of various internal states via manifold learning, to achieve the aim of humancentered interaction studies. A critical review of widely used emotion models is described, then, facial expression features of various internal states via the locally linear embedding （LLE） are extracted. The recognition of facial expressions is created with the pleasure-displeasure and arousal-sleep dimensions in a two-dimensional model of emotion. The recognition result of various internal state expressions that mapped to the embedding space via the LLE algorithm can effectively represent the structural nature of the two-dimensional model of emotion. Therefore our research has established that the relationship between facial expressions of various internal states can be elaborated in the two-dimensional model of emotion, via the locally linear embedding algorithm.

ON NETWORK-BASED KERNEL METHODS FOR PROTEIN-PROTEIN INTERACTIONS WITH APPLICATIONS IN PROTEIN FUNCTIONS PREDICTION

Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding （LLE） kernel method, diffusion kernel and laplacian kernel to uncover the relationship between proteins functions and protein-protein interactions （PPI）. The author first construct kernels based on PPI networks, then apply support vector machine （SVM） techniques to classify proteins into different functional groups. The 5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chi-square methods. Finally, the authors conduct predictions of functions of some unknown genes and verify the preciseness of our prediction in part by the information of other data source.