Two phase cooperative learning for supervised dimensionality reduction

Abstract

The simultaneous minimization of the reconstruction and classification error is a hard non convex problem, especially when a non-linear mapping is utilized. To overcome this obstacle, motivated by the widespread success of Cooperative Neural Networks, an innovative supervised dimensionality reduction framework is proposed, based on a cooperative two phase optimization strategy. Specifically, the proposed framework that requires minimal parameter adjustment consists of an autoencoder for dimensionality reduction and a separator network for separability assessment of the embedding. This scheme results in meaningful and discriminable codes, which are optimized for the classification task and are exploitable by any trainable classifier. The experimental results showed that the proposed methodology achieved competitive results against the state-of-the-art competing methods, while being much more efficient in terms of parameter count. Finally, it was empirically justified that the proposed methodology introduces advanced behavioural explainability, while enabling applicability for image generation tasks.