Related Articles
Parzen Windows density estimation technique
• Last Updated : 30 Jan, 2020

Parzen Window is a non-parametric density estimation technique. Density estimation in Pattern Recognition can be achieved by using the approach of the Parzen Windows. Parzen window density estimation technique is a kind of generalization of the histogram technique.

It is used to derive a density function, . is used to implement a Bayes Classifier. When we have a new sample feature and when there is a need to compute the value of the class conditional densities, is used. takes sample input data value and returns the density estimate of the given data sample.

An n-dimensional hypercube is considered which is assumed to possess k-data samples.
The length of the edge of the hypercube is assumed to be hn. Hence the volume of the hypercube is: Vn = hnd

We define a hypercube window function, φ(u) which is an indicator function of the unit hypercube which is centered at origin.:
φ(u) = 1 if |ui| <= 0.5
φ(u) = 0 otherwise
Here, u is a vector, u = (u1, u2, …, ud)T.
φ(u) should satisfy the following:

1. 2. Let Since, φ(u) is centered at the origin, it is symmetric.
φ(u) = φ(-u)

• is a hypercube of size h cenetered at u0
• Let D = {x1, x2, …, xn} be the data samples.
• For any would be 1 only if falls in a hypercube of side centered at .
• Hence the number of data points falling in a hypercube of side h centered at x is Hence the estimated density function is : Also Since, Vn = hnd, Density Function becomes :  would satisfy the following conditions:

1. 2.  Attention reader! Don’t stop learning now. Get hold of all the important CS Theory concepts for SDE interviews with the CS Theory Course at a student-friendly price and become industry ready.

My Personal Notes arrow_drop_up