Extending previous works done on considerably smaller data sets, the paper studies linear discriminant analysis of about 30 hours of phoneme-labeled speech data in the time-frequency domain. Analysis is carried both independently in time and frequency and jointly. Data driven spectral basis show similar frequency sensitivity as human hearing. LDA-derived temporal FIR filters are consistent with...

Extending previous works done on considerably smaller data sets, the paper studies linear discriminant analysis of about 30 hours of phoneme-labeled speech data in the time-frequency domain. Analysis is carried both independently in time and frequency and jointly. Data driven spectral basis show similar frequency sensitivity as human hearing. LDA-derived temporal FIR filters are consistent with...

In this paper, a framework of Discriminant Subspace Analysis (DSA) method is proposed to deal with the Small Sample Size (SSS) problem in face recognition area. Firstly, it is rigorously proven that the null space of the total covariance matrix, St, is useless for recognition. Therefore, a framework of Fisher discriminant analysis in a low-dimensional space is developed by projecting all the sa...

ABSTRACT- Firmness is one of the most important quality indicators for apple fruits, which is highly correlated with the storage time. The acoustic impulse response technique is one of the most commonly used nondestructive detection methods for evaluating apple firmness. This paper presents a non-destructive method for classification of Iranian apple (Malus domestica Borkh. cv. Golab) according...

An automated wood recognition system is designed to classify tropical wood species.The wood features are extracted based on two feature extractors: Basic Grey Level Aura Matrix (BGLAM) technique and statistical properties of pores distribution (SPPD) technique. Due to the nonlinearity of the tropical wood species separation boundaries, a pre classification stage is proposed which consists ofKme...

Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture models (FMM) do not require, or even use if avai...

In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis (LDA) and Kernel Discriminant Analysis (KDA). The methods used for classification are Nearest Neighbor (NN) and Support V...

We present a novel discriminant analysis learning method which is applicable to non-linear data structures. The method can deal with pattern classification problems which have a multi-modal distribution for each class and samples of other classes may be closer to a class than those of the class itself. Conventional linear discriminant analysis (LDA) and LDA mixture model can not solve this line...

The kernel discriminant analysis (KDA), an extension of the linear discriminant analysis (LDA) and null space-based LDA into the kernel space, generally provides good pattern recognition (PR) performance for both small sample size (SSS) and non-SSS PR problems. Due to the eigen-decomposition technique adopted, however, the original scheme for the feature extraction with the KDA suffers from a h...