As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …

Realizing high performance of ordinary robots is one of the core problems in robotic
research. Improving the performance of ordinary robots usually relies on the collaborative …

In recent years, a variety of nonlinear dimensionality reduction techniques have been
proposed that aim to address the limitations of traditional techniques such as PCA. The …

In recent years, a variety of nonlinear dimensionality reduction techniques have been
proposed that aim to address the limitations of traditional techniques such as PCA and …

Feature extraction and feature selection have been regarded as two independent
dimensionality reduction methods in most of the existing literature. In this paper, we propose …

Face recognition has received significant attention because of its numerous applications in
access control, law enforcement, security, surveillance, Internet communication and …

Due to the high spectral resolution, anomaly detection from hyperspectral images provides a
new way to locate potential targets in a scene, especially those targets that are spectrally …

Sparse representation (or coding)-based classification (SRC) has gained great success in
face recognition in recent years. However, SRC emphasizes the sparsity too much and …

Spectral analysis-based dimensionality reduction algorithms are important and have been
popularly applied in data mining and computer vision applications. To date many algorithms …

Nearest neighbor graphs are widely used in data mining and machine learning. A brute-
force method to compute the exact kNN graph takes Θ (dn2) time for n data points in the d …