A Classification Algorithm in Li-K Nearest Neighbor

The KNN (The K nearest neighbor) is known as its simple efficient and widely used in classification problems or as a benchmark in classification problems. For different data types especially complex structure and high-dimensional data in real-life, the choice of distance metrics between sample points is a relatively complexity problem. The KNN´s feature space is generally n-dimensional real vector space. This article converts the samples in the vector space to be the elements in line with the Lie group nature and then proposes a Li-KNN algorithm to solve the classification problem based on the theory of Lie groups. It shows good results by the experimental on handwritten numeral.