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The non-parametric density estimation is
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[[Category:ECE662]]
 
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P(x) = k/(NV)
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where, k is the number of samples in V, N is the total number of samples, and V is the volume surrounding x.
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This estimate is computed by two approaches
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1) Parzen window approach
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  - Fixing the volume V and determining the number k of data points inside V
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2) KNN(K-Nearest Neighbor)
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- Fixing the value of k and determining the minimum volume V that encompasses k points in the dataset
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* The advantages of non-parametric techniques
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- No assumption about the distribution required ahead of time
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- With enough samples we can converge to an target density
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* The disadvantages of non-parametric techniques
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- If we have a good classification, the number of required samples may be very large
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- Computationally expensive
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Latest revision as of 08:45, 10 April 2008

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BSEE 2004, current Ph.D. student researching signal and image processing.

Landis Huffman