Techniques that can introduce low dimensional feature representation with enhanced discriminatory power are important in face recognition systems. This paper presents one of the symbolic factor analysis method i.e., symbolic Linear Discriminant Analysis (symbolic LDA) method for face representation and recognition. Classical factor analysis methods extract features, which are single valued in n...

Taxonomies are becoming essential to a growing number of application, particularly for specific domains. Taxonomies, originally built by hand, have been recently focused on their automatic generation. In particular, a main issue on automatic taxonomy building regards the choice of the most suitable features. In this paper, we propose an analysis on how each feature changes its role along taxono...

BACKGROUND/AIMS The histopathological assessment of endoscopic colorectal biopsies is important in the distinction between normality and chronic idiopathic inflammatory bowel disease, and between ulcerative colitis and Crohn's disease, in subjects with symptoms of bowel dysfunction. This study aims to use carefully defined histopathological observations on a large study population to produce sy...

The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer's disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher...

This paper proposes an alternative approach to face recognition algorithm that is based on global/holistic features of face image and simplified linear discriminant analysis (LDA). The proposed method can overcome main problems of the conventional LDA in terms of large processing time for retraining when a new class data is registered into the training data set. The holistic features of face im...

Fisher’s discriminant analysis is very powerful for classification but it does not perform well when the number of classes is large but the number of samples in each class is small. We propose to resolve this problem by dynamically grouping classes at different levels in a tree. We recast the problem of classification as a regression problem so that the classification (class labels as output) a...

This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) systems based on electroencephalography (EEG). In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the c...

This paper focuses on the identification of disfluent sequences and their distinct structural regions, based on acoustic and prosodic features. Reported experiments are based on a corpus of university lectures in European Portuguese, with roughly 32h, and a relatively high percentage of disfluencies (7.6%). The set of features automatically extracted from the corpus proved to be discriminant of...

This paper proposes a boosted linear discriminant analysis (LDA) solution on features extracted by the multilinear principal component analysis (MPCA) to enhance gait recognition performance. Three dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then, lower-dimensional vectorial features are obtained through discriminative feature sel...

Detecting faces in images is a key step in numerous computer vision applications as face recognition for example. Face detection is a difficult task in image analysis because of the large face intra-class variability which is due to the important influence of the environmental conditions on the face aspect. The existing methods for face detection can be divided into holistic methods and feature...