| Line 2: | Line 2: | ||
P(x) = k/(NV) | P(x) = k/(NV) | ||
| − | where, k is the number of samples in V | + | |
| + | 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 | This estimate is computed by two approaches | ||
Revision as of 21:18, 7 April 2008
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
