A nonlinear adaptive estimation method based on local approximation

One of the most important problems in signal processing is to estimate the output for a query from the input/output (I/O) data seen so far. This paper presents a nonlinear adaptive estimation method based on the n-nearest neighbor approach. In this method, observed I/O data are stored in a database in the form of a X-dimensional binary digital search trie (k-D trie), and a nonlinear local model to answer each query is derived based on regularization theory. The database contents are efficiently time updated to follow nonstationary data. A storage procedure allowing a simple and efficient update is developed for reduction in processing time and storage requirement. The effectiveness of the proposed method is demonstrated with both simulation data and real speech signals.