Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in ma...

Background: Osteoporosis knowledge test is a comprehensive questionnaire that evaluates risk factors, nutrition and exercise recommendations, as well as general subjects such as bone evolution, diagnosis, and osteoporosis treatment. The aim of this study was to determine the psychometric characteristics of the revised version of osteoporosis knowledge test (OKT) in Iranian adolescent population...

Hyperspectral images possess the characteristics of high dimensionality, which causes “dimensional disaster” and low classification accuracy, in response to problems, based on traditional k-means algorithm considering importance different bands for classification, also combining both intra-class inter-class information, a Kmeans-CM (K-means with correlation coefficient maximize distance) spectr...

Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability within the non-lesion class ,based on prior knowledge of the data, to support an automated lesion detection system. A multi-class convolutional neural network (CNN) is proposed to categorize input image patches into su...

Many powerful techniques in supervised learning (e.g. linear discriminant analysis, LDA, and quadratic classifier) assume that data in each class have a single Gaussian distribution. In reality, data in the class of interest, i.e., the object class, could have non-Gaussian distributions and could be isolated into several subgroups by the data from other classes (the context classes). To address...

Recently, low-rank and sparse model-based dimensionality reduction (DR) methods have aroused lots of interest. In this paper, we propose an effective supervised DR technique named block-diagonal constrained low-rank and sparse-based embedding (BLSE). BLSE has two steps, i.e., block-diagonal constrained low-rank and sparse representation (BLSR) and block-diagonal constrained low-rank and sparse ...

Based on linear regression, a novel method called reconstructive discriminant analysis (RDA) is developed for feature extraction and dimensionality reduction (DR). RDA is induced from linear Regression classification (LRC). LRC assumes each class lies on a linear subspace and finds the nearest subspace for a given sample. But the original space cannot guarantee that the given sample matches its...