A new sparse kernel probability density function pdf estimator based on zero-norm constraint is constructed using the classical Parzen window PW estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.
['Xia Hong', 'Sheng Chen', 'Chris J. Harris']
Using zero-norm constraint for sparse probability density function estimation