Hebbian Learning with Kernel-Based Embedding of Input Data

Although it requires simple computations, provides good performance on linear classification tasks and offers a suitable environment for active learning strategies, the Hebbian learning rule is very sensitive to how the training data relate to each other in the input space. Since this spatial arrangement is inherent to each set of samples, the practical application of this learning paradigm is limited. Thus, representation learning may play an important role in projecting the input data into a new space where linear separability is improved. Earlier methods based on orthogonal coding addressed this issue but presented many side effects, impoverishing the generalization of the model. Hence, this paper considers a recently proposed method based on kernel density estimators, which performs a likelihood-based projection where linear separability and generalization capacity are enhanced in an autonomous fashion. Results show that this novel method allows one to use linear classifiers to solve many binary classification problems and overcome the performance of well-established classifiers.