Active Search for Nearest Neighbors
This addresses a fundamental bottleneck in pattern recognition and machine learning for applications requiring efficient similarity search.
The paper tackles the computational inefficiency of traditional nearest neighbor search methods by proposing an innovative approach inspired by the human visual system, which actively searches around the point of interest to reduce comparisons.
In pattern recognition or machine learning, it is a very fundamental task to find nearest neighbors of a given point. All the methods for the task work basically by comparing the given point to all the points in the data set. That is why the computational cost increases with the number of data points. However, the human visual system seems to work in a different way. When the human visual system tries to find the neighbors of one point on a map, it directly focuses on the area around the point and actively searches the neighbors by looking or zooming in and out around the point. In this paper, we propose an innovative search method for nearest neighbors, which seems very similar to how human visual system works on the task.