A　new　manifold　learning　algorithm　is　proposed　in　this　paper.　Our　method　is　motivated　by　the　unit　covariance　constraint　problem　of　spectral　embedding　methods,　where　a　unit　covariance　constraint　is　imposed　to　avoid　degenerate　solutions　that　map　all　manifold　samples　to　one　point.　This　constraint　distorts　the　aspect　ratio　and　introduces　unwanted　correlation　between　different　components　of　embedding　coordinates.　Instead,　our　method　uses　boundary　conditions　to　pull　apart　mapped　points,　and　obtains　the　embedding　by　solving　linear　systems　under　boundary　conditions.　The　mapping　of　boundary　samples　is　decided　by　that　of　a　coarse　version　of　manifold,　obtained　by　a　graph　simplification　algorithm　designed　by　us.　Comparisons　between　our　method　and　several　other　representative　manifold　learning　methods　are　made,　and　the　results　demonstrate　the　effectiveness　of　the　proposed　method.