Numerous　corrupted　or　noisy　images　are　generated　owing　to　the　problems　in　sampling　and　transmission.　Facing　the　category　of　high-dimensional　image　data,　directly　operating　like　classification　is　inefficient　and　the　accuracy　is　normally　not　ideal.　Consequently,　dimensionality　reduction　is　a　critical　process.　After　dimensionality　reduction　by　classical　algorithms,　the　classification　efficiency　can　be　improved.　However,　the　problem　of　low　accuracy　still　exists.　In　this　paper,　an　unsupervised　denoising　feature　learning　model　is　proposed　to　learn　a　discriminative　feature　representation　from　corrupted　images.　First,　the　relationship　between　initial　data　and　low-dimensional　feature　representation　is　explored.　We　introduce　several　classical　dimensionality　reduction　algorithms.　Second,　the　working　mechanism　of　denoising　feature　learning　model　is　proved.　Due　to　its　structural　characteristics,　denoising　model　can　extract　robust　features　from　partially　destroyed　data　to　represent　the　original　data.　It　demonstrates　proposed　model　can　extract　essential　information　from　noisy　image　to　decrease　reconstruction　error.　Finally,　extensive　experiments　demonstrate　that　the　features　representation　obtained　by　the　proposed　method　outperforms　those　obtained　by　the　existing　classical　dimensionality　reduction　algorithms.　©　2021　Elsevier　Inc.