We propose a new interdisciplinary approach for the hard optimization problem of tree-structured clustering, wherein the imposition of structural constraints on the solution drastically reduces the complexity of classifying data. The method, derived with analogy to statistical physics, performs a global optimization over the entire tree. Experimentation with non-trivial examples shows that our approach consistently outperforms greedy methods and avoids local minima that may trap them.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence