In this chapter we discuss another popular data mining algorithm that can be used for
supervised or unsupervised learning. Linear Discriminant Analysis (LDA) was proposed by R. …

… Analysis (PCA) and Linear Discriminant Analysis (LDA) are … Linear Discriminant Analysis
easily handles the case where … The use of Linear Discriminant Analysis for data classification is …

Suppose we are given a learning set $$\mathcal{L}$$ of multivariate observations (ie, input
values $$\mathfrak{R}^r$$ ), and suppose each observation is known to have come from …

… techniques such as Mixture Discriminant Analysis (MDA) [25] and Neural Networks (NN) [27],
but the most famous technique of this approach is the Linear Discriminant Analysis (LDA) […

Linear discriminant analysis (LDA) is a versatile statistical method for reducing redundant
and noisy information from an original sample to its essential features. Particularly, LDA is a …

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 …

Linear dimensionality reduction methods, such as LDA, are often used in object recognition
for feature extraction, but do not address the problem of how to use these features for …

Dimensionality reduction is an important aspect in the pattern classification literature, and
linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction …

… Linear discriminant analysis (LDA) seeks an optimal linear transformation by which the … The
goal of LDA is to find a linear transformation that maximizes class separability in the reduced …

Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction
and classification. LDA in the binaryclass case has been shown to be equivalent to linear …