This is a tutorial and survey paper for Locally Linear Embedding (LLE) and its variants. The
idea of LLE is fitting the local structure of manifold in the embedding space. In this paper, we …

Approaches to combine local manifold learning (LML) and the k-nearest-neighbor (k NN)
classifier are investigated for hyperspectral image classification. Based on supervised LML …

Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear
dimensionality reduction. It has a number of attractive features: it does not require an …

In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear
manifold learning methods have aroused a great deal of interest in the machine learning …

Accurate and timely misfire fault diagnosis is of vital significance for diesel engines.
However, existing algorithms are prone to fall into model over-fitting and adopt low energy …

In most complex classification problems, many types of features have been captured or
extracted. Feature fusion is used to combine features for better classification and to reduce …

This paper presents a novel fault detection method based on incremental locally linear
embedding (I-LLE) to improve the accuracy of fault detection for a satellite with high …

Providing the kernel function of the local tangent space alignment (LTSA) algorithm, the
generalisation of both LTSA and supervised LTSA (SLTSA) is achieved. Moreover, using the …

Raw data sets taken with various capturing devices are usually multidimensional and need
to be preprocessed before applying subsequent operations, such as clustering …