Sparse learning based fuzzy c-means clustering

Recently sparse representation (SR) based clustering has attracted a growing interests in the field of image processing and pattern recognition. Since the SR technology has favorable category distinguishing ability, we introduce it into the fuzzy clustering in this paper, and propose a new clustering algorithm, called sparse learning based fuzzy c-means (SL_FCM). Firstly, to reduce the computation complexity of the SR based FCM method, most energy of discriminant feature obtained by solving a SR model is reserved and the remainder is discarded. By this way, some redundant information (i.e. the correlation among samples of different classes) in the discriminant feature can be also removed, which can improve the clustering quality. Furthermore, to further enhance the clustering performance, the position information of valid values in discriminant feature is also used to re-define the distance between sample and clustering center in SL_FCM. The weighted distance in SL_FCM can enhance the similarity of the samples from the same class and the difference of the samples of different classes, thus to improve the clustering result. In addition, as the dimension of stored discriminant feature of each sample is different, we use set operations to formulate the distance and cluster center in SL_FCM. The comparisons on several datasets and images demonstrate that SL_FCM performs better than other state-of-art methods with higher accuracy, while keeps low spatial and computational complexity, especially for the large scale dataset and image.