Another initialization approach is to use a specific method that learns pattern distribution from the data and uses it as the filter’s initialization. The principal component analysis (PCA) was used to initialize a self-organizing map as a filter, and faster convergence was observed. K-means-based pre-initialization was experimented with and compared with random initialization. The K-means initialization uses an orthonormal matrix to rotate centroids symmetrically for an optimal solution through iteration. The generated filters are shown in Figure 2. Some filters learned with random initialization are prone to be noisy, as highlighted in red. K-means-based initialization would reduce noisy filters and result in faster convergence. It was also noted that any clustering method could produce a similar outcome.

FIGURE 2 filters learned after random initialization (Top) and after using K-means-based initialization (Bottom)