Learning graph embeddings for high-dimensional data is an important technology for
dimensionality reduction. The learning process is expected to preserve the discriminative …

Graph embedding in dimensionality reduction has attracted much attention in the high-
dimensional data analysis. Graph construction in graph embedding plays an important role …

Graph embedding is a very useful dimensionality reduction technique in pattern recognition.
In this paper, we develop a novel discriminative dimensionality reduction technique entitled …

Graph embedding plays an important role in dimensionality reduction for processing the
high-dimensional data. In graph embedding, its keys are the different kinds of graph …

In many pattern recognition applications feature selection and instance selection can be
used as two data preprocessing methods that aim at reducing the computational cost of the …

Collaborative representation based techniques have shown promising results for face
recognition; however, most of them code the samples by taking the overall samples as a …

Sparsity preserving projection (SPP), as a widely used linear unsupervised dimensionality
reduction (DR) method, is designed to preserve the sparse reconstructive relationship of the …

Facing with the absence of supervised information to guide the search of relevant features
and the grid-search of model/hyper-parameters, it is more preferred to develop parameter …

To preserve the sparse representation of the original high-dimensional data, sparsity
preserving projection (SPP) is proposed which involves solving a series of minimization …

Many fundamental problems in machine learning require some form of dimensionality
reduction. To this end, two different strategies were used: Manifold Learning and Feature …