  Sparse representation-based classification (SRC) has attracted much attention
by casting the recognition problem as simple linear regression problem. SRC
methods, however, still is limited to enough labeled samples per category,
insufficient use of unlabeled samples, and instability of representation. For
tackling these problems, an unlabeled data driven inverse projection
pseudo-full-space representation-based classification model is proposed with
low-rank sparse constraints. The proposed model aims to mine the hidden
semantic information and intrinsic structure information of all available data,
which is suitable for few labeled samples and proportion imbalance between
labeled samples and unlabeled samples problems in frontal face recognition. The
mixed Gauss-Seidel and Jacobian ADMM algorithm is introduced to solve the
model. The convergence, representation capability and stability of the model
are analyzed. Experiments on three public datasets show that the proposed
LR-S-PFSRC model achieves stable results, especially for proportion imbalance
of samples.
