A robust estimator based on density and scale optimization and its application to clustering

In this paper, we propose a new robust algorithm that estimates the prototype parameters of a given structure from a possibly noisy data set. The new algorithm has several attractive features. It does not make any assumptions on the proportion of noise in the data set. Instead, it dynamically estimates a scale parameter and the weights/memberships associated with each data point, and softly rejects outliers based on these weights. The algorithm essentially optimizes a density criterion, since it tries to minimize the size while maximizing the cardinality. Moreover, the proposed algorithm is computationally simple, and can be extended to perform parameter estimation when the data set consists of multiple clusters