A noise robust method for pattern discovery in quantized time series: the concept matrix approach

An efficient method for pattern discovery from discrete time series is introduced in this paper. The method utilizes two parallel streams of data, a discrete unit time-series and a set of labeled events, From these inputs it builds associative models between systematically co-occurring structures existing in both streams. The models are based on transitional probabilities of events at several different time scales. Learning and recognition processes are incremental, making the approach suitable for online learning tasks. The capabilities of the algorithm are demonstrated in a continuous speech recognition task operating in varying noise levels.