Abstract-Nonparametric density gradient estimation using a generalized kernel approach is investigated. Conditions on the kernel functions are derived to guarantee asymptotic unbiasedness, consistency, and uniform consistenby of the estimates. The results are generalized to obtain a simple mean-shift estimate that can be extended in a k-nearestneighbor approach. Applications of gradient estimation to pattern recognition are presented using clustering and intrinsic dimensionality problems, with the ultimate goal of providing further understanding of these problems in terms of density gradients.
Fukunaga and Hostetler REF presented a gradient estimation approach to clustering and its application to pattern recognition.
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Trans. Inf. Theory
IEEE Trans. Inf. Theory
Computer Science Mathematics