Matrix analysis
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Aircraft identification integrated into an airport surface surveillance video system
Machine Vision and Applications
Tracking the activity of participants in a meeting
Machine Vision and Applications
Queues and Artificial Potential Trenches for Multirobot Formations
IEEE Transactions on Robotics
Boundary following and globally convergent path planning using instant goals
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Weighted locally linear embedding for dimension reduction
Pattern Recognition
Geometrically local embedding in manifolds for dimension reduction
Pattern Recognition
Information Processing and Management: an International Journal
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In this paper, an unsupervised learning algorithm, neighborhood linear embedding (NLE), is proposed to discover the intrinsic structures such as neighborhood relationships, global distributions and clustering property of a given set of input data. This algorithm eases the process of intrinsic structure discovery by avoiding the trial and error operations for neighbor selection, and at the same time, allows the discovery to adapt to the characteristics of the input data. In addition, it is able to explore different intrinsic structures of data simultaneously, and the discovered structures can be used to compute manipulative embeddings for potential data classification and recognition applications. Experiments for image object segmentation are carried out to demonstrate some potential applications of the NLE algorithm.