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Accurate inference of genomic ancestry is critically important in human genetics, epidemiology, and related fields. Geneticists today have access to multiple heterogeneous population-based datasets from studies collected under different protocols. Therefore, joint analyses of these datasets require robust and consistent inference of ancestry, where a common strategy is to yield an ancestry space generated by a reference dataset. However, such a strategy is sensitive to batch artefacts introduced by different protocols. In this work, we propose a novel robust genome-wide ancestry inference method; referred to as SUGIBS, based on an unnormalized genomic (UG) relationship matrix whose spectral (S) decomposition is generalized by an Identity-by-State (IBS) similarity degree matrix. SUGIBS robustly constructs an ancestry space from a single reference dataset, and provides a robust projection of new samples, from different studies. In experiments and simulations, we show that, SUGIBS is robust against individual outliers and batch artifacts introduced by different genotyping protocols. The performance of SUGIBS is equivalent to the widely used principal component analysis (PCA) on normalized genotype data in revealing the underlying structure of an admixed population and in adjusting for false positive findings in a case-control admixed GWAS. We applied SUGIBS on the 1000 Genome project, as a reference, in combination with a large heterogeneous dataset containing auxiliary 3D facial images, to predict population stratified average or ancestry faces. In addition, we projected eight ancient DNA profiles into the 1000 Genome ancestry …