Pattern Recognition, 4th Edition

6.5. Independent Component Analysis

As we have already seen, the principal component analysis (PCA) performed by the Karhunen–Loève transform produces features y(i), i = 0, 1,…, N − 1, that are mutually uncorrelated. The solution obtained by the KL transform solution is optimal when dimensionality reduction is the goal and one wishes to minimize the approximation mean square error. However, for certain applications, such as the one illustrated in Figure 6.1, the obtained solution falls short of the expectations. In contrast, the more recently developed independent component analysis (ICA) theory, for example, [Hyva 01, Como 94, Jutt 91, Hayk 00, Lee 98], tries to achieve much more than simple decorrelation of the data. The ICA task is casted ...