Revision as of 22:18, 7 April 2008 by Gong1 (Talk)

The non-parametric density estimation is

P(x) = k/(NV) where, k is the number of samples in V,N is the total number of samples, V is the volume surrounding x

This estimate is computed by two approaches

1) Parzen window approach

; Fixing the volume V and determining the number k of data points inside V

2) KNN(K-Nearest Neighbor)

;Fixing the value of k and determining the minimum volume V that encompasses k points in the dataset


  • The advantages of non-parametric techniques
- No assumption about the distribution required ahead of time
- With enough samples we can converge to an target density
  • The disadvantages of non-parametric techniques
- If we have a good classification, the number of required samples may be very large
- Computationally expensive

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