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Deterministic Multi-kernel based extreme learning machine for pattern classification
- Source :
- Expert Systems with Applications. 183:115308
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- The Extreme learning machine (ELM) designed by Huang et al. is proved to be a fast and good classifier over a decade, but existing ELM is non-deterministic in nature as well as kernel dependent and needs attention to optimize the selection of kernels. In ELM feature space is obtained with the help of single kernel function. The choice of kernel depends on perceptiveness of classification problem. So a generalized framework with deterministic nature along with optimized kernel is ought to be designed that can be applied to large domain of real world heterogeneous pattern classification problems. This paper presents a deterministic extreme learning machine for neural network with feedforward architecture which is formulated with multiple kernel learning. We further enhance this approach by incorporating Gray level co-occurrence matrix (GLCM) for multi-modal feature extraction. Two formulation of kernel extreme learning machine are introduced, with target kernel function as a linear combination of different base kernels. The first one is based on deterministic multiple kernel learning while the second one uses deterministic multiple kernel learning along with GLCM for extracting the invariant feature vectors. The performance of proposed algorithms are analyzed on pattern recognition problem for face recognition by changing the number of training set, types of kernel used and coefficients used for combining base kernels. The superior recognition rate is achieved for prominent multi-class face databases, when compared with contemporary methods that prove the efficacy of proposed algorithms.
- Subjects :
- 0209 industrial biotechnology
Multiple kernel learning
Artificial neural network
Computer science
business.industry
Feature vector
Feature extraction
General Engineering
Pattern recognition
02 engineering and technology
Facial recognition system
Computer Science Applications
020901 industrial engineering & automation
Artificial Intelligence
Kernel (statistics)
Classifier (linguistics)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Extreme learning machine
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 183
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........1e20ace9edb92969fa0399b3ad8e23b6