Feature space transformation for semi-supervised learning for protein subcellular localization in fluorescence microscopy images

As rapid acquisition of large collections of fluorescence microscopy cell images can be automated, large-scale subcellular localizations of GFP-tagged fusion proteins can be practically accomplished. Semi-supervised learning has the potential of using a large set of unlabeled images for the recognition of subcellular organelle patterns, but the performance still has room for improvement. This paper presents a feature space transformation method based on the spectral graph theory to improve semi-supervised learning. Experimental result shows that our feature space transformation method can improve the classification accuracy substantially.