Anisotropic oracle inequalities in noisy quantization
share
The effect of errors in variables in quantization is investigated. We prove general exact and non-exact oracle inequalities with fast rates for an empirical minimization based on a noisy sample Z_i=X_i+ϵ_i,i=1,...,n, where X_i are i.i.d. with density f and ϵ_i are i.i.d. with density η. These rates depend on the geometry of the density f and the asymptotic behaviour of the characteristic function of η. This general study can be applied to the problem of k-means clustering with noisy data. For this purpose, we introduce a deconvolution k-means stochastic minimization which reaches fast rates of convergence under standard Pollard's regularity assumptions.
READ FULL TEXT