Feature　representation　is　generally　applied　to　reducing　the　dimensions　of　high-dimensional　data　to　accelerate　the　process　of　data　handling　and　enhance　the　performance　of　pattern　recognition.　However,　the　dimensionality　of　data　nowadays　appears　to　be　a　rapidly　increasing　trend.　Existing　unsupervised　feature　representation　methods　are　susceptible　to　the　rapidly　increasing　dimensionality　of　data,　which　may　result　in　learning　a　meaningless　feature　that　in　turn　affect　their　performance　in　other　applications.　In　this　paper,　an　unsupervised　adversarial　auto-encoder　network　is　studied.　This　network　is　a　probability　model　that　combines　generative　adversarial　networks　and　variational　auto-encoder　to　perform　variational　inference　and　aims　to　generate　reconstructed　data　similar　to　original　data　as　much　as　possible.　Due　to　its　adversarial　training,　this　model　is　relatively　robust　in　feature　learning　compared　with　other　methods.　First,　the　architecture　and　training　strategy　of　adversarial　auto-encoder　are　presented.　We　attempt　to　learn　a　discriminative　feature　representation　for　high-dimensional　image　data　via　adversarial　auto-encoder　and　take　its　advantage　into　image　clustering,　which　has　become　a　difficult　computer　vision　task　recently.　Then　amounts　of　comparative　experiments　are　carried　out.　The　comparison　contains　eight　feature　representation　methods　and　two　recently　proposed　deep　clustering　methods　performed　on　eight　different　publicly　available　image　data　sets.　Finally,　to　evaluate　their　performance,　we　utilize　a　K-means　clustering　on　the　low-dimensional　feature　learned　from　each　feature　representation　algorithm,　and　select　three　evaluation　metrics　including　clustering　accuracy,　adjusted　rand　index　and　normalized　mutual　information,　to　provide　a　comparison.　Comprehensive　experiments　prove　the　usefulness　of　the　learned　discriminative　feature　via　adversarial　auto-encoder　in　the　tested　data　sets.