9. The method of claim 7 , wherein the undirected graph is represented by G 0 ; wherein the feature is represented by ν i , wherein the weight of the node ν i is represented by ω i , wherein the weight of the edge between a first node ν i and a second node ν j is represented by e i,j ; wherein the set is represented by S; wherein a node with a largest weight is ν k i ; and further comprising identifying nodes that have similarities to the node with the largest weight ν k i ; updating weights of the identified nodes by: 
 ω j ←ω j −e k t′ j *2c,j≠k i ; adding the identified nodes with the largest weight ν k i to the set S; removing the node with the largest weight ν k i from the undirected graph together with edges connected to the node by: S i+1 =S i {ν k i }, G i+1 =G i {ν k i }; and outputting the set S t to solve the optimization problem.
7. The method of claim 6 , wherein the greedy algorithm further comprises: selecting the node with a largest weight without loss of generality; handicapping the other nodes according to the similarities; adding the selected node to the set and removing the selected node from the graph together with edges connected to the selected node; and outputting the set that contains the selected features.