Over the past few years, it has been established that a number of long intergenic non-coding RNAs (lincRNAs) are linked to a wide variety of human diseases. The relationship among many other lincRNAs still remains as puzzle. Validation of such link between the two entities through biological experiments is expensive. However, piles of information about the two are becoming available, thanks to the High Throughput Sequencing (HTS) platforms, Genome Wide Association Studies (GWAS), etc., thereby opening opportunity for cutting-edge machine learning and data mining approaches. However, there are only a few in silico lincRNA-disease association inference tools available to date, and none of these utilizes side information of both the entities. The recently developed Inductive Matrix Completion (IMC) technique provides a recommendation platform among two entities considering respective side information. But, the formulation of IMC is incapable of handling noise and outliers that may present in the dataset, while data sparsity consideration is another issue with the standard IMC method. Thus, a robust version of IMC is needed that can solve these two issues. As a remedy, in this paper, we propose Robust Inductive Matrix Completion (RIMC) using l2,1 norm loss function as well as l2,1 norm based regularization. We applied RIMC to the available association data between human lincRNAs and OMIM disease phenotypes as well as a diverse set of side information about the lincRNAs and the diseases. Our method performs better than the state-of-the-art methods in terms of precision@k and recall@k at the top- k disease prioritization to the subject lincRNAs. We also demonstrate that RIMC is equally effective for querying about novel lincRNAs, as well as predicting rank of a newly known disease for a set of well-characterized lincRNAs. Availability: All the supporting datasets are available at the publicly accessible URL located at http://biomecis.uta.edu/~ashis/res/RIMC/.