In this paper, the main application image processing, manifold learning and the method of Gaussian mixture model for dimensionality reduction and cluster analysis, the image color information are all studied. First, the color data access algorithm is introduced, secondly, the manifold learning in the local linear embedding (LLE) algorithm is used in color analysis; then the results of an evalua...

The Locally Linear Embedding (LLE) algorithm is an unsupervised nonlinear dimensionality-reduction method, which reports a low recognition rate in classification because it gives no consideration to the label information of sample distribution. In this paper, a classification method of supervised LLE (SLLE) based on Linear Discriminant Analysis (LDA) is proposed. First, samples are classified a...

In this study liquid phase equilibrium compositions were measured at 298.15 K under atmospheric pressure for (water + propan-1-ol + diethyl carbonate (DEC) + benzene or cyclohexane or heptane) quaternary systems and (water + DEC + propan-1-ol or benzene or cyclohexane) ternary systems. Good correlation of the experimental LLE data was seen for the measured systems by both modified and extended ...

Cancer classification is one major application of microarray data analysis. Due to the ultra high dimensionality nature of microarray data, data dimension reduction has drawn special attention for such type of data analysis. The currently available data dimension reduction methods are either supervised, where data need to be labeled, or computational complex. In this paper, we proposed to use a...

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...

A study was conducted to find the optimal approach for integrating semantic technology across the extensive data banks utilized by the Laboratory for Laser Energetics (LLE). In addition to the immense amount of information stored by LLE, there are multiple repositories of data, making it difficult to locate and process information critical to the operations of the laser facilities. Semantic tec...

In this paper, we propose a novel linear subspace learning algorithm called Flexible Orthogonal Neighborhood Preserving Embedding (FONPE), which is a linear approximation of Locally Linear Embedding (LLE) algorithm. Our novel objective function integrates two terms related to manifold smoothness and a flexible penalty defined on the projection fitness. Different from Neighborhood Preserving Emb...

fMRI data is represented in a space with very high dimensionality. Because of this, classifiers such as SVM and Naive Bayes may overfit this data. Dimensionality reduction methods are intended to extract features from data in a high dimensional space. Training a classifier on data in a lower dimension may improve the true error of the classifier beyond the performance obtained by training in a ...

A glycoprotein has been isolated from the colonic lavages of healthy individuals that is immunologically equivalent to carcinoembryonic antigen purified from tumor tissue. The NH2-terminal sequence of the glycoprotein from normal colon lavages is Lys-Leu-Thr-lle-Glu-Ser-Thr-Pro-Phe-(Asn)-Val-Ala-Glu-Gly-Lys-Glu-Val-(Leu,lle)-(Leu,lle)-(Leu,lle)-Val-(His,Arg?)-?-(Leu,lle). This is homologous to ...