Dimensionality　reduction　plays　a　significant　role　for　the　performance　of　large-scale　image　retrieval.　In　this　paper,　various　dimensionality　reduction　methods　are　compared　to　validate　their　own　performance　in　image　retrieval.　For　this　purpose,　first,　the　Scale　Invariant　Feature　Transform　(SIFT)　features　and　HSV　(Hue,　Saturation,　Value)　histogram　are　extracted　as　image　features.　Second,　the　Principal　Component　Analysis　(PCA),　Fisher　Linear　Discriminant　Analysis　(FLDA),　Local　Fisher　Discriminant　Analysis　(LFDA),　Isometric　Mapping　(ISOMAP),　Locally　Linear　Embedding　(LLE),　and　Locality　Preserving　Projections　(LPP)　are　respectively　applied　to　reduce　the　dimensions　of　SIFT　feature　descriptors　and　color　information,　which　can　be　used　to　generate　vocabulary　trees.　Finally,　through　setting　the　match　weights　of　vocabulary　trees,　large-scale　image　retrieval　scheme　is　implemented.　By　comparing　multiple　sets　of　experimental　data　from　several　platforms,　it　can　be　concluded　that　dimensionality　reduction　method　of　LLE　and　LPP　can　effectively　reduce　the　computational　cost　of　image　features,　and　maintain　the　high　retrieval　performance　as　well.