Outlier identification algorithms (OIAs) can help astronomers looking for worthwhile targets of study in a sea of data by focusing investigations on smaller sets of objects that do not follow the trends of the larger population. We applied a Principal Component Analysis (PCA) and an unsupervised Random Forest (uRF) to high-significance sources in the Chandra Source Catalog v.2 (CSC2). We found 119 sources that appeared in every application of the uRF OIA. We compare these 119 outliers with the rest of the analyzed CSC2 sources and crossmatch them with the SIMBAD astronomical database. We investigated 5 outliers located within the Chandra ACIS field of view of the Galactic center as accreting-white-dwarf candidates, using spectral analysis to characterize the systems and estimate white dwarf mass.