A Flexible Metric Nearest-Neighbor Classification based on the Decision Boundaries of SVM for Hyperspectral Image

摘要

The k-nearest neighbor classifier is a simple and appealing approach to classification problems. It expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensional situation. Using a locally adaptive metric becomes crucial in order to keep class conditional probabilities close to uniform, thereby minimizing the bias of estimates. A technique that computes a locally flexible metric by means of the decision boundaries of support vector machines (SVMs) is proposed. Then the modified neighborhoods can be shrunk in directions orthogonal to these decision boundaries and elongated parallel to the boundaries. Thereafter, any neighborhood-based classifier can use the modified neighborhoods.