Linear discriminant analysis

About: Linear discriminant analysis is a research topic. Over the lifetime, 18361 publications have been published within this topic receiving 603195 citations. The topic is also known as: Linear discriminant analysis & LDA.

An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection

Abstract: Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident-detection problem can be viewed as the recognition of incident patterns from incident-free patterns. A neural network classifier must be trained first using incident and incident-free traffic data. The dimensionality of the training input is high, and the embedded incident characteristics are not readily detectable. This paper presents a computational model for automatic traffic incident detection using discrete wavelet transform (DWT), linear discriminant analysis (LDA), and neural networks. DWT and LDA are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used for traffic incident detection. Simulated and actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the model yields a detection rate of nearly 100% and a false-alarm rate of about 1% for 2- or 3-lane freeways.

Human Facial Expression Recognition Using Stepwise Linear Discriminant Analysis and Hidden Conditional Random Fields

Abstract: This paper introduces an accurate and robust facial expression recognition (FER) system. For feature extraction, the proposed FER system employs stepwise linear discriminant analysis (SWLDA). SWLDA focuses on selecting the localized features from the expression frames using the partial $\boldsymbol {F}$ -test values, thereby reducing the within class variance and increasing the low between variance among different expression classes. For recognition, the hidden conditional random fields (HCRFs) model is utilized. HCRF is capable of approximating a complex distribution using a mixture of Gaussian density functions. To achieve optimum results, the system employs a hierarchical recognition strategy. Under these settings, expressions are divided into three categories based on parts of the face that contribute most toward an expression. During recognition, at the first level, SWLDA and HCRF are employed to recognize the expression category; whereas, at the second level, the label for the expression within the recognized category is determined using a separate set of SWLDA and HCRF, trained just for that category. In order to validate the system, four publicly available data sets were used, and a total of four experiments were performed. The weighted average recognition rate for the proposed FER approach was 96.37% across the four different data sets, which is a significant improvement in contrast to the existing FER methods.

Correlation synthetic discriminant functions.

Abstract: Advanced filters are described for distortion-invariant space-invariant object identification and location in clutter using correlators. These correlation synthetic discriminant functions (SDFs) are extensions of earlier projection SDFs. They provide control of the sidelobe levels and the shape of the output correlation function as well as its peak intensity. The theory for synthesis of three such SDFs and a discussion of correlation plane detection criteria for use with these filters are presented.