As one of the most popular nonlinear dimensionality reduction methods, Isomap has been
widely used in pattern recognition and machine learning. However, Isomap has the …

The traditional manifold learning methods usually utilize the original observed data to
directly define the intrinsic structure among data. Because the original samples often contain …

As one of the most promising nonlinear unsupervised dimensionality reduction (DR)
technique, the Isomap reveals the intrinsic geometric structure of manifold by preserving …

Manifold learning is widely used in machine learning and pattern recognition. However,
manifold learning only considers the similarity of samples belonging to the same class and …

Dimensionality reduction is one of the most important techniques in the field of data mining.
It embeds high-dimensional data into a low-dimensional vector space while keeping the …

Images are often represented as vectors with high dimensions when involved in
classification. As a result, dimensionality reduction methods have to be developed to avoid …

We propose a unified manifold learning framework for semi-supervised and unsupervised
dimension reduction by employing a simple but effective linear regression function to map …

Isomap is a well-known nonlinear dimensionality reduction (DR) method, aiming at
preserving geodesic distances of all similarity pairs for delivering highly nonlinear manifolds …

As an effective image clustering tool, low-rank representation (LRR) can capture the intrinsic
representation of the observed samples. However, firstly, the good representation does not …

Manifold learning is a widely used technique for dimensionality reduction as it can reveal the
intrinsic geometric structure of data. However, its performance decreases drastically when …