While locality preserving projection (LPP) is directly applicable to only vector data, two-
dimensional locality preserving projection (2DLPP) is directly applicable to two-dimensional …

Solving problems with small sample sizes during training for feature extraction and the
dimensionality reduction method will not produce high face recognition accuracy using the …

Linear discriminant analysis (LDA) often suffers from the small sample size problem when
dealing with high-dimensional face data. Random subspace can effectively solve this …

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

In this paper, we introduce a novel linear discriminant approach called Two-Dimensional
Neighborhood Margin and Variation Embedding (2DNMVE), which explicitly considers the …

In this paper, we proposed a new Two-Dimensional Linear Discriminant Analysis (2DLDA)
method, based on Two-Dimensional Principle Component Analysis (2DPCA) concept. In …

As an effective feature extraction method, locality sensitive discriminant analysis (LSDA)
utilizes the neighbor relationship of data to characterize the manifold structure of data and …

On face recognition, most previous works on dimensionality reduction and classification
would first transform the input image into 1-D vector, which ignores the underlying data …

Marginal Fisher analysis (MFA) is a representative margin-based learning algorithm for face
recognition. A major problem in MFA is how to select appropriate parameters, k 1 and k 2, to …

Most supervised manifold learning-based methods preserve the original neighbor
relationships to pursue the discriminating power. Thus, structure information of the data …