Feature selections : Still a high dimentionality
There seems to be a misconception.  You can use $k$-nearest neighbors with any number of dimensions.  There is no prohibition on using it with a large number of features.  And in some cases $k-NN will work well even in high dimensions (a large number of features); it all depends on the dataset, and to find out whether it will work well, you have to try it.  In general, feature selection might make the algorithm work better, or it might make it work worse -- it all depends.