Linear Discriminant Analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with high dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more gene...

In this slecture, basic principles of implementing nearest neighbor rule will be covered. The error related to the nearest neighbor rule will be discussed in detail including convergence, error rate, and error bound. Since the nearest neighbor rule relies on metric function between patterns, the properties of metrics will be studied in detail. Example of different metrics will be introduced wit...

Clustering is often formulated as a discrete optimization problem: given a finite set of sample points, the objective is to find, among all partitions of the data set, the best one according to some quality measure. However, in the statistical setting where we assume that the finite data set has been sampled from some underlying space, the goal is not to find the best partition of the given sam...

We explore the eeect of dimensionality on the \nearest neigh-bor" problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as di-mensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective , we present empirical results on both real and s...

The 1-N-N classifier is one of the oldest methods known. The idea is extremely simple: to classify X find its closest neighbor among the training points (call it X ,) and assign to X the label of X .

A fundamental activity common to image processing, pattern recognition, and clustering algorithm involves searching set of n , k-dimensional data for one which is nearest to a given target data with respect to distance function . Our goal is to find search algorithms with are full search equivalent -which is resulting match as a good as we could obtain if we were to search the set exhausting. 1...

Definition 1.1. Nearest Neighbor Search: Given a set of points {x1, . . . , xn} ∈ R preprocess them into a data structure X of size poly(n, d) in time poly(n, d) such that nearest neighbor queries can be performed in logarithmic time. In other words, given a search point q a radius r and X one can return an xi such the ||q − xi|| ≤ r or nothing if no such point exists. The search for xi should ...

conclusions by comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy. objectives through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. materials an...