Semi-supervised Non-negative Local Coordinate Factorization

Non-negative matrix factorization (NMF) is a popular matrix decomposition technique that has attracted extensive attentions from data mining community. However, NMF suffers from the following deficiencies: (1) it is non-trivial to guarantee the representation of the data points to be sparse, and (2) NMF often achieves unsatisfactory clustering results because it completely neglects the labels of the dataset. Thus, this paper proposes a semi-supervised non-negative local coordinate factorization (SNLCF) to overcome the above deficiencies. Particularly, SNLCF induces the sparse coefficients by imposing the local coordinate constraint and propagates the labels of the labeled data to the unlabeled ones by indicating the coefficients of the labeled examples to be the class indicator. Benefit from the labeled data, SNLCF can boost NMF in clustering the unlabeled data. Experimental results on UCI datasets and two popular face image datasets suggest that SNLCF outperforms the representative methods in terms of both average accuracy and average normalized mutual information.