Efficient Learning on Point Sets
Recently several methods have been proposed to learn from data that are represented as sets of multidimensional vectors. Such algorithms usually suffer from the high demand of computational resources, making them impractical on large-scale problems. We propose to solve this problem by condensing i.e. reducing the sizes of the sets while maintaining the learning performance. Three methods are examined and evaluated with a wide spectrum of set learning algorithms on several large-scale image data sets. We discover that k-Means can successfully achieve the goal of condensing. In many cases, k-Means condensing can improve the algorithms' speed, space requirements, and surprisingly, learning performances simultaneously.