Ion-channel sensors which mimic naturally occurring pore-forming proteins can be used to detect small metal ions and organic molecules. A chamber with a lipid bilayer hosting ion-channels produced by protein insertion constitutes such a sensor. Each analyte produces a characteristic signal pattern during its migration from one section of the chamber to another through the ion-channels. A four chamber ion-channel sensor array is built for accurate analyte detection. The power distribution information in the transform domain has been successfully used as discriminatory features for each chamber signal. However, these features are not robust to noise and hence result in a reduced classification performance. In this paper, we pose the stabilization of PSD features extracted from noisy segments as a matrix completion problem. Matrix completion with a low rank assumption provides the stabilized features. We demonstrate using a synthetic experiment that the proposed setup achieves improved classification performance in comparison to using the features directly. Furthermore, performing analyte detection in real ion-channel data, using the proposed robust features, provides reduction in false alarm rates.