What Are Recurrent Expansion Algorithms? Exploring a Deeper Space than Deep Learning ^†

Machine learning applications usually become a subject of data unavailability, complexity, and drift, resulting from massive and rapid changes in data volume, velocity, and variety (3V). Recent advances in deep learning have brought many improvements to the field, providing generative modeling, nonlinear abstractions, and adaptive learning to address these challenges. In fact, deep learning aims to learn from representations that provide a consistent abstraction of the original feature space, which makes it more meaningful and less complex. However, data complexity related to different distortions, such as higher levels of noise, remains difficult to overcome. In this context, recurrent expansion (RE) algorithms have recently been developed to explore deeper representations than ordinary deep networks, providing further improvement in feature mapping. Unlike traditional deep learning, which extracts meaningful representations through inputs abstraction, RE enables entire deep networks to be merged into another one consecutively, allowing the exploration of inputs, maps, and estimated targets (IMTs) as primary sources of learning; three sources of information that provide additional knowledge about their interactions in a deep network. In addition, RE makes it possible to study IMTs of several networks and learn significant features, improving its accuracy with each round. In this context, this paper presents a general overview of RE, its main learning rules, advantages, disadvantages, and its future opportunities, while reviewing the state-of the art and providing some illustrative examples.