Using zero-norm constraint for sparse probability density function estimation |
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Authors: | X Hong S Chen CJ Harris |
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Affiliation: | 1. School of Systems Engineering University of Reading , Reading RG6 6AY , UK x.hong@reading.ac.uk;3. School of Electronics and Computer Science University of Southampton , Southampton SO17 1BJ , UK |
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Abstract: | 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. |
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Keywords: | cross-validation Parzen window probability density function sparse modelling |
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