Quasi-Monotonic Segmentation Talk in Ottawa

I’m giving a talk next week at the Text Analysis and Machine Learning Group (TAMALE) seminar at the University of Ottawa. I will talk on Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation. It is not directly related to text and machine learning, but many of the ideas from time series data mining port over to text processing. After all, a sequence is a sequence. I see Joel Martin wil also give a talk there this Spring on “Libminer”. Here’s the abstract for my talk:

Monotonicity is a simple yet significant qualitative characteristic. We consider the problem of segmenting an array in up to K segments. We want segments to be as monotonic as possible and to alternate signs. We propose a quality metric for this problem, present an optimal linear time algorithm based on novel formalism, and compare experimentally its performance to a linear time top-down regression algorithm. We show that our algorithm is faster and more accurate. Applications include pattern recognition and qualitative modeling.

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