Generalized mean based back-propagation of errors for ambiguity resolution

- The Ambiguity Resolution problem is formally defined.
- A new multi-layer ambiguity resolving perceptron is proposed.
- A continuous and differentiable generalized mean based error function is introduced.
- Back-propagation algorithm for the proposed error function is formulated.
- The new method is compared with 4 alternatives to show its usefulness.

Ambiguity in a dataset, characterized by data points having multiple target labels, may occur in many supervised learning applications. Such ambiguity originates naturally or from misinterpretation, faulty encoding, and/or incompleteness of data. However, most applications demand that a data point be assigned a single label. In such cases, the supervised learner must resolve the ambiguity. To effectively perform ambiguity resolution, we propose a new variant of the popular Multi-Layer Perceptron model, called the Generalized Mean Multi-Layer Perceptron (GMMLP). In GMMLP, a novel differentiable error function guides the back-propagation algorithm towards the minimum distant target for each data point. We evaluate the performance of the proposed algorithm against three alternative ambiguity resolvers on 20 new artificial datasets containing ambiguous data points. To further test for scalability and comparison with multi-label classifiers, 18 real datasets are also used to evaluate the new approach.