We propose an approach for sparse representation of dense features for action classification. Sparse representation has already been shown in literature as a good approximation for signals for various computer vision applications. This property is leveraged to represent a dense feature like action bank in the form of sparse dictionaries. These dictionaries are learnt using on-line dictionary learning (ODL) which further facilitates incorporating new training examples into existing dictionaries for more robust representation of various categories of action as and when required. Evaluation of the proposed method on realistic action datasets like UCF50 and HMDB51 shows that considering sparse representation of a dense feature is more suitable for classification than the feature itself. Copyright 2015 ACM.