Pineapple is a fruit commodity that Indonesia's flagship. This because pineapple has the highest export volume in Indonesia. To obtain pineapples with perfect ripeness, generally manually selected, this becomes inefficient if large numbers of are selected. So, study, an image processing system will be developed can classify ripeness based on its image. In color feature extraction used hue and s...

In this paper we recall two kernel methods for discriminant analysis. The first one is the kernel counterpart of the ubiquitous Linear Discriminant Analysis (Kernel-LDA), while the second one is a method we named Kernel Springy Discriminant Analysis (Kernel-SDA). It seeks to separate classes just as Kernel-LDA does, but by means of defining attractive and repulsive forces. First we give technic...

Kernel methods have become standard tools for solving classification and regression problems in statistics. An example of a kernel based classification method is Kernel Fisher discriminant analysis (KFDA), a kernel based extension of linear discriminant analysis (LDA), which was proposed by Mika et al. (1999). As in the case of LDA, the classification performance of KFDA deteriorates in the pre...

Linear discriminant analysis (LDA) is a classical approach for dimensionality reduction. It aims to maximize between-class scatter and minimize within-class scatter, thus maximize the class discriminant. However, for undersampled problems where the data dimensionality is larger than the sample size, all scatter matrices are singular and the classical LDA encounters computational difficulty. Rec...

Identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. Discriminant analysis is widely used for solving such problems. This paper will review two basic parametric methods: LDA (linear discriminant analysis) and QDA (quadratic discriminant analysis). Their usage in recognition of splice sites and exons in the human genome will be demonst...

Linear discriminant analysis (LDA) is one of the most popular supervised dimensionality reduction (DR) techniques used in computer vision, machine learning, and pattern classification. However, LDA only captures global geometrical structure information of the data and ignores the geometrical variation of local data points of the same class. In this paper, a new supervised DR algorithm called lo...

The dimensionality of sample is often larger than the number of training samples for highdimensional pattern recognition such as face recognition. Here linear discriminant analysis (LDA) cannot be performed directly because of the singularity of the within-class scatter matrix. This is socalled “small sample size” (SSS) problem. PCA plus LDA (FDA) and Direct LDA (DLDA) are two popular methods t...

Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as face recognition and image retrieval. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are singular. A well-known approach to deal ...

A generalized discriminant analysis based on a new optimization criterion is presented. The criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) when the scatter matrices are singular. An efficient algorithm for the new optimization problem is presented. The solutions to the proposed criterion form a family of algorithms for generalized LDA, which can ...

This paper proposes an innovative algorithm named 2D-LDA, which directly extracts the proper features from image matrices based on Fisher s Linear Discriminant Analysis. We experimentally compare 2D-LDA to other feature extraction methods, such as 2D-PCA, Eigenfaces and Fisherfaces. And 2D-LDA achieves the best performance. 2004 Elsevier B.V. All rights reserved.