Introduction to Pattern Recognition: A Matlab Approach

108 CHAPTER 4 Feature Selection

Solution. Generate the data set.

randn('seed',0);

m=1; var=0.16;

stdevi=sqrt(var);

norm_dat=m+stdevi*randn(1,100);

Generate the outliers.

outl=[6.2 -6.4 4.2 15.0 6.8];

Add outliers at the end of the data.

dat=[norm_dat';outl'];

Scramble the data.

rand('seed',0); % randperm() below calls rand()

y=randperm(length(dat));x=dat(y);

Identify outliers and their corresponding indices.

times=1; % controls the tolerance threshold

[outliers,Index,new_dat]=simpleOutlierRemoval(x,times);

[outliers Index]

The new_dat file contai ns the data after the outliers have been rejected. The program output should

look like thi s:

outliers index

4.2 3

6.8 ...