Subspace identification has been used extensively because its ability to detail the internal subspace structure of data, which can be in a variety applications such as dimension reduction, anomaly detection and so on. However, many advanced algorithms are limited on their applicability large data sets due computation memory requirements with respect number input points. To overcome this problem...

Abstruct-The finite sample performance of a nearest neighbor classifier is analyzed for a two-class pattern recognition problem. An exact integral expression is derived for the m-sample risk R, given that a reference m-sample of labeled points is available to the classifier. The statistical setup assumes that the pattern classes arise in nature with fixed a priori probabilities and that points ...

In this paper we propose and analyze a novel method for automatic stock trading which combines technical analysis and the nearest neighbor classification. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists...

Suppose a bank has a database of people’s details and their credit rating. These details would probably be the person’s financial characteristics such as how much they earn, whether they own or rent a house, and so on, and would be used to calculate the person’s credit rating. However, the process for calculating the credit rating from the person’s details is quite expensive, so the bank would ...

We present a novel method for shape detection in natural scenes based on incomplete contour fragments and nearest neighbour search. In contrast to popular methods which employ sliding windows, chamfer matching and SVMs, we characterise each contour fragment by a local descriptor and perform a fast nearest-neighbour search to find similar fragments in the training set. Based on this idea, we sho...