Data　fusion　is　used　to　integrate　features　from　heterogenous　data　sources　into　a　consistent　and　accurate　representation　for　certain　learning　tasks.　As　an　effective　technique　for　data　fusion,　unsupervised　multimodal　feature　representation　aims　to　learn　discriminative　features,　indicating　the　improvement　of　classification　and　clustering　performance　of　learning　algorithms.　However,　it　is　a　challenging　issue　since　varying　modality　favors　different　structural　learning.　In　this　paper,　we　propose　an　efficient　feature　learning　method　to　represent　multimodal　images　as　a　sparse　multigraph　structure　embedding　problem.　First,　an　effective　algorithm　is　proposed　to　learn　a　sparse　multigraph　construction　from　multimodal　data,　where　each　modality　corresponds　to　one　regularized　graph　structure.　Second,　incorporating　the　learned　multigraph　structure,　the　feature　learning　problem　for　multimodal　images　is　formulated　as　a　form　of　matrix　factorization.　An　efficient　corresponding　algorithm　is　developed　to　optimize　the　problem　and　its　convergence　is　also　proved.　Finally,　the　proposed　method　is　compared　with　several　state-of-the-art　single-modal　and　multimodal　feature　learning　techniques　in　eight　publicly　available　face　image　datasets.　Comprehensive　experimental　results　demonstrate　that　the　proposed　method　outperforms　the　existing　ones　in　terms　of　clustering　performance　for　all　tested　datasets.