T-distributed　stochastic　neighbor　embedding　(t-SNE)　is　an　effective　visualization　method.　However,　it　is　non-parametric　and　cannot　be　applied　to　steaming　data　or　online　scenarios.　Although　kernel　t-SNE　provides　an　explicit　projection　from　a　high-dimensional　data　space　to　a　low-dimensional　feature　space,　some　outliers　are　not　well　projected.　In　this　paper,　bi-kernel　t-SNE　is　proposed　for　out-of-sample　data　visualization.　Gaussian　kernel　matrices　of　the　input　and　feature　spaces　are　used　to　approximate　the　explicit　projection.　Then　principal　component　analysis　is　applied　to　reduce　the　dimensionality　of　the　feature　kernel　matrix.　Thus,　the　difference　between　inliers　and　outliers　is　revealed.　And　any　new　sample　can　be　well　mapped.　The　performance　of　the　proposed　method　for　out-of-sample　projection　is　tested　on　several　benchmark　datasets　by　comparing　it　with　other　state-of-the-art　algorithms.