  For image recognition, an extensive number of methods have been proposed to
overcome the high-dimensionality problem of feature vectors being used. These
methods vary from unsupervised to supervised, and from statistics to
graph-theory based. In this paper, the most popular and the state-of-the-art
methods for dimensionality reduction are firstly reviewed, and then a new and
more efficient manifold-learning method, named Soft Locality Preserving Map
(SLPM), is presented. Furthermore, feature generation and sample selection are
proposed to achieve better manifold learning. SLPM is a graph-based
subspace-learning method, with the use of k-neighbourhood information and the
class information. The key feature of SLPM is that it aims to control the level
of spread of the different classes, because the spread of the classes in the
underlying manifold is closely connected to the generalizability of the learned
subspace. Our proposed manifold-learning method can be applied to various
pattern recognition applications, and we evaluate its performances on facial
expression recognition. Experiments on databases, such as the Bahcesehir
University Multilingual Affective Face Database (BAUM-2), the Extended
Cohn-Kanade (CK+) Database, the Japanese Female Facial Expression (JAFFE)
Database, and the Taiwanese Facial Expression Image Database (TFEID), show that
SLPM can effectively reduce the dimensionality of the feature vectors and
enhance the discriminative power of the extracted features for expression
recognition. Furthermore, the proposed feature-generation method can improve
the generalizability of the underlying manifolds for facial expression
recognition.
