Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

In this paper a novel multikernel deterministic extreme learning machine (ELM) and its variants are developed for classification of non-linear problems. Over a decade ELM is proved to be efficacious learning algorithms, but due to the non-deterministic and single kernel dependent feature mapping proprietary, it cannot be efficiently applied to real time classification problems that require invariant output solution. We address this problem by analytically calculation of input and hidden layer parameters for achieving the deterministic solution and exploiting the data fusion proficiency of multiple kernel learning. This investigation originates a novel deterministic ELM with single layer architecture in which kernel function is aggregation of linear combination of disparate base kernels. The weight of kernels depends upon perspicacity of problem and is empirically calculated. To further enhance the performance we utilize the capabilities of fuzzy set to find the pixel-wise coalition of face images with different classes. This handles the uncertainty involved in face recognition under varying environment condition. The pixel-wise membership value extracts the unseen information from images up to significant extent. The validity of the proposed approach is tested extensively on diverse set of face databases: databases with and without illumination variations and discrete types of kernels. The proposed algorithms achieve 100% recognition rate for Yale database, when seven and eight images per identity are considered for training. Also, the superior recognition rate is achieved for AT & T, Georgia Tech and AR databases, when compared with contemporary methods that prove the efficacy of proposed approaches in uncontrolled conditions significantly.