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A fast algorithm for sparse probability density function construction

Hong, X. and Chen, S. (2013) A fast algorithm for sparse probability density function construction. In: 18th International Conference on Digital Signal Processing (DSP2013), 1 - 3 July 2013, Santorini - Greece.

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A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:34105
Uncontrolled Keywords:Terms—probability density function, sparse modelling, minimum integrated square error

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