Indicisive classifier

Nearest neighbor classification expects the class conditional probabilities to be locally constant. The assumption becomes invalid in high dimension due to the curse−of−dimensionality. Severe bias can be introduced under this condition when using nearest neighbor rule. We propose an adaptive nearest neighbor classification method ¡°indecisive classifier¡± to minimize bias and variance by avoiding decision making in some hard−decision region. As a result, better classification performance can be expected in some scenario such as video based face recognition.