Principal Component Analysis Method with Gauss Quantum Particle Swarm Optimization
| Autor: | Jie Cheng, Yan-Jie Yang, Rong-Jia Zhu, Dan Li |
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| Rok vydání: | 2019 |
| Předmět: |
050101 languages & linguistics
Computer science Gaussian Dimensionality reduction 05 social sciences MathematicsofComputing_NUMERICALANALYSIS Particle swarm optimization Swarm behaviour 02 engineering and technology symbols.namesake Principal component analysis 0202 electrical engineering electronic engineering information engineering symbols Unsupervised learning 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Gradient descent Global optimization Algorithm |
| Zdroj: | 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing. |
| DOI: | 10.1109/iccwamtip47768.2019.9067724 |
| Popis: | Principal component analysis is an unsupervised machine learning algorithm for linear dimensionality reduction of data. The essence of principal component analysis is to solve the problem of maximum variance of projection point. Generally, the gradient rise method is used to solve the optimal parameters of the objective function, but it is easy to fall into the local maximum trap. In this paper, the principal component analysis method with Gaussian quantum particle swarm is proposed to overcome the shortcomings of gradient ascent method, which makes the algorithm independent of gradient and has stronger global optimization performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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