The techniques Conformal Eigenmap and Neighborhood Preserving Embedding (NPE) have been proposed as extensions of local non-linear techniques. Many of the commonly used non-linear dimensionality reduction, such as Local Linear Embedding (LLE) and Laplacian eigenmap are not explicitly designed to preserve local features such as distances or angles. In first proposed Conformal Eigenmap technique,...

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preservin...

Locally Linear Embedding (LLE) is a nonlinear dimensionality reduction method proposed recently. It can reveal the intrinsic manifold of data, which can’t be provided by classical linear dimensionality reduction methods. The application of LLE, however, is limited because of its lack of a mapping between the observation and the low-dimensional output. In this paper, we propose a method to estab...

In this paper, two significant weaknesses of locally linear embedding (LLE) applied to computer vision are addressed: ‘‘intrinsic dimension’’ and ‘‘eigenvector meanings’’. ‘‘Topological embedding’’ and ‘‘multi-resolution nonlinearity capture’’ are introduced based on mathematical analysis of topological manifolds and LLE. The manifold topological analysis (MTA) method is described and is based ...

A rapid assay procedure was developed for mycotoxin citrinin in corn using liquid-liquid extraction (LLE) cartridges. Ground corn was extracted with methylene chloride and 0.5 N phosphoric acid. The extract was added to an LLE cartridge containing a diatomaceous-earth adsorbant, previously impregnated with sodium bicarbonate solution. After aspiration to dryness, the cartridge was eluted with m...

We describe a method of processing hyperspectral images of natural scenes that uses a combination of kmeans clustering and locally linear embedding (LLE). The primary goal is to assist anomaly detection by preserving spectral uniqueness among the pixels. In order to reduce redundancy among the pixels, adjacent pixels which are spectrally similar are grouped using the k-means clustering algorith...

The locally linear embedding (LLE) algorithm can be used to discover a low-dimensional subspace from face manifolds. However, it does not mean that a good accuracy can be obtained when classifiers work under the subspace. Based on the proposed ULLELDA (Unified LLE and linear discriminant analysis) algorithm, an ensemble version of the ULLELDA (En-ULLELDA) is proposed by perturbing the neighbor ...

Abstract Locally linear embedding (LLE) is a highly popular manifold learning and nonlinear dimensionality reduction technique. However, the neighborhood parameters of the algorithm are sensitive to the mapping results and difficult to choose. In this paper, we propose an adaptive neighborhood selection method based on Silhouette index for LLE algorithm. From the point of the cluster quality of...

PURPOSE The aim of this study was to investigate the language outcomes of 7-year-old children with and without a history of late language emergence at 24 months. METHOD One hundred twenty-eight children with a history of late language emergence (LLE) at 24 months and 109 children with a history of normal language emergence (NLE) at 24 months participated in direct behavioral assessment of mul...

In view of the complexity and nonlinearity of rolling bearings, this paper presents a new supervised locally linear embedding method (R-NSLLE) for feature extraction. In general, traditional LLE can capture the local structure of a rolling bearing. However it may lead to limited effectiveness if data is sparse or non-uniformly distributed. Moreover, like other manifold learning algorithms, the ...