Line 3: Line 3:
 
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
+
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 22:19, 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

Alumni Liaison

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva