A new supervised LLE method based on the fisher projection was proposed in this paper, and combined it with a new classification algorithm based on manifold learning to realize the recognition of the plant leaves. Firstly, the method utilizes the Fisher projection distance to replace the sample's geodesic distance, and a new supervised LLE algorithm is obtained .Then, a classification algorithm...

The dimension of the population genetics data produced by next-generation sequencing platforms is extremely high. However, the "intrinsic dimensionality" of sequence data, which determines the structure of populations, is much lower. This motivates us to use locally linear embedding (LLE) which projects high dimensional genomic data into low dimensional, neighborhood preserving embedding, as a ...

The local linear embedding algorithm (LLE) is a non-linear dimension-reducing technique, widely used due to its computational simplicity and intuitive approach. LLE first linearly reconstructs each input point from its nearest neighbors and then preserves these neighborhood relations in the low-dimensional embedding. We show that the reconstruction weights computed by LLE capture the high-dimen...

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...

The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural computation. Here we describe locally linear embedding (LLE), an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data. The data, assum...

The locally linear embedding (LLE) algorithm is useful for analyzing sets of very different geoscientific images, ranging from smooth potential field images, to sharp outputs from modeling fracturing and fluid flows via cellular automata, to hand sketches of geological sections. LLE maps the very high-dimensional space embedding the images into 2-D, arranging the images on a plane. This arrange...

We propose an extension of the algorithm for nonlinear dimensional reduction locally linear embedding (LLE) based on the usage of the geodesic distance (ISOLLE). In LLE, each data point is reconstructed from a linear combination of its n nearest neighbors, which are typically found using the Euclidean distance. We show that the search for the neighbors performed with respect to the geodesic dis...

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 ...