Traditional　supervised　dimensionality　reduction　methods　can　establish　a　better　model　often　under　the　premise　of　a　large　number　of　samples.　However,　in　real-world　applications　where　labeled　data　are　scarce,　traditional　methods　tend　to　perform　poorly　because　of　overfitting.　In　such　cases,　unlabeled　samples　could　be　useful　in　improving　the　performance.　In　this　paper,　we　propose　a　semi-supervised　dimensionality　reduction　method　by　using　partial　least　squares　(PLS)　which　we　call　semi-supervised　partial　least　squares　(S2PLS).　To　combine　the　labeled　and　unlabeled　samples　into　a　S2PLS　model,　we　first　apply　the　PLS　algorithm　to　unsupervised　dimensionality　reduction.　Then,　the　final　S2PLS　model　is　established　by　ensembling　the　supervised　PLS　model　and　the　unsupervised　PLS　model　which　using　the　basic　idea　of　principal　model　analysis　(PMA)　method.　Compared　with　unsupervised　or　supervised　dimensionality　reduction　algorithms,　S2PLS　not　only　can　improve　the　prediction　accuracy　of　the　samples　but　also　enhance　the　generalization　ability　of　the　model.　Meanwhile,　it　can　obtain　better　results　even　there　are　only　a　few　or　no　labeled　samples.　Experimental　results　on　five　UCI　data　sets　also　confirmed　the　above　properties　of　S2PLS　algorithm.