Learning and Memory of Spatial Relationship by a Neural Network with Sparse Features
Research on efficiency of learning and memory is very important for theoretic exploration and practical application. This paper gives a discussion on learning and memory of spatial relationships between initial positions and object positions by a neural network with sparse features. As an example, the paper discusses how the neural network learns the visual contexts between human eye centers and random initial positions surrounding the eye centers in images with as little memory as possible. Some sparse features are designed and distances between initial positions and the labeled eye centers in horizontal and vertical directions are learned and memorized respectively. Such a system could predict object positions from a new initial position according to the contexts that the neural network learned. A group of experiments on efficiency of learning and memory with sparse features in several single and integrated scales are analyzed and discussed.