关键词: 非负l1图, 谱聚类, l1最小化, 手写字符聚类

Abstract: The construction of information graph is critical for many machine learning tasks.Based on the sparse representation theory，a directed nonnegative l1-graph is proposed.In the procedure of constructing the graph，each sample is first represented by the nonnegative linear combination of the remaining samples，and then the neighboring samples and the corresponding similarities are simultaneously obtained by solving an l1-minimization problem.Finally，spectral clustering with nonnegative l1-graph is applied to handwritten character clustering.The experimental results demonstrate that the proposed method has better clustering performance and lower computation complexity compared with spectral clustering with l1-graph.

Key words: nonnegative l1-graph, spectral clustering, l1-minimization, handwritten character clustering