Unsupervised nonlinear adaptive manifold learning for global and local information

Abstract

In this paper, we propose an Unsupervised Nonlinear Adaptive Manifold Learning method (UNAML) that considers both global and local information. In this approach, we apply unlabeled training samples to study nonlinear manifold features, while considering global pairwise distances and maintaining local topology structure. Our method aims at minimizing global pairwise data distance errors as well as local structural errors. In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data, we add a feature approximate error that can be used to learn a linear extractor. Also, we add a feature approximate error that can be used to learn a linear extractor. In addition, we use a method of adaptive neighbor selection to calculate local structural errors. This paper uses the kernel matrix method to optimize the original algorithm. Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

Road Segmentation and Environment Labeling for Autonomous Vehicles

“…Autoencoders (AE) are categorized into many types, and their use varies, but perhaps the more common use involves feature learning or representation learning [22] for clustering similar points together. Feature learning [23] is significant for segmentation of the road scenes; among multiple technologies [24][25][26][27] prevailing in the community, the unsupervised learning technology such as AE [28] has a wide range of applications, especially in clustering nonlinear subspaces. Autoencoders are also used to compress point clouds [29] and can reconstruct high-quality point clouds from the latent space.…”

Low‐rank preserving embedding regression for robust image feature extraction

“…The above methods only consider the structure information from a single aspect (i.e., local or global one), which ignore the important information from other views. To this end, Gao et al [13] proposed an unsupervised non-linear adaptive manifold learning method (UNAML) that takes the global and local information into consideration. It applies unlabelled training samples to study non-linear manifold features while simultaneously exploring the global pairwise distances and preserving the local topology.…”

“…To this end, Gao et al. [13] proposed an unsupervised non‐linear adaptive manifold learning method (UNAML) that takes the global and local information into consideration. It applies unlabelled training samples to study non‐linear manifold features while simultaneously exploring the global pairwise distances and preserving the local topology.…”

Dimension reduction algorithm of battlefield situation data based on local preserving discriminant projection

“…As a nonlinear dimension reduction method developed in recent years, manifold learning carries out mathematical modeling through local linear assumption, carries out global nonlinear mapping through corresponding rules, and mining the inherent laws of data to effectively remove redundant information and achieve dimension reduction [2] . In the processing of battlefield situation data, the method of manifold learning is adopted to reduce the dimension of situation data, which can reduce the size of original data, reduce the amount of calculation and improve the efficiency of decision making.…”