Monitoring activities using video data is an important surveillance problem. A special
scenario is to learn the pattern of normal activities and detect abnormal events from a very …

In this paper we present the conbination of deep learning and Support Vector Machine
applied on the recognition of hyperspectal images. Hyperspectral image recognition is an …

In this paper, we improve the minimum squared error (MSE) algorithm for classification by
modifying its classification rule. Differing from the conventional MSE algorithm which first …

Kernel Fisher discriminant analysis (KFDA) extracts a nonlinear feature from a sample by
calculating as many kernel functions as the training samples. Thus, its computational …

We propose Kernel Self-optimized Locality Preserving Discriminant Analysis (KSLPDA) for
feature extraction and recognition. The procedure of KSLPDA is divided into two stages, ie …

Kernel learning is becoming an important research topic in the area of machine learning,
and it has wide applications in pattern recognition, computer vision, image and signal …

This study aimed to construct a kernel Fisher discriminant analysis (KFDA) method from well
logs for lithology identification purposes. KFDA, via the use of a kernel trick, greatly improves …

Kernel principal component analysis (KPCA) has been widely applied in pattern recognition
areas, but it endures the high store space and time consuming problems on feature …

Breast tissue classification is an important and effective way for computer aided diagnosis of
breast cancer. We present Semi-supervised Locality Discriminant Projections with Kernels …

The changes of face images with poses and polarized illuminations increase data
uncertainty in face recognition. In fact, synthesized mirror samples can be recognized as …