Slow Feature Analysis: Perspectives for Technical Applications of a Versatile Learning Algorithm
| Autor: | Laurenz Wiskott, Alberto N. Escalante-B. |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Computer Science::Machine Learning
Quantitative Biology::Neurons and Cognition business.industry Computer science Dimensionality reduction Feature extraction Supervised learning Cognitive neuroscience of visual object recognition Machine learning computer.software_genre Blind signal separation k-nearest neighbors algorithm Statistics::Machine Learning Artificial Intelligence Feature (computer vision) Traffic sign recognition Artificial intelligence business Algorithm computer |
| Zdroj: | KI - Künstliche Intelligenz. 26:341-348 |
| ISSN: | 1610-1987 0933-1875 |
| DOI: | 10.1007/s13218-012-0190-7 |
| Popis: | Slow Feature Analysis (SFA) is an unsupervised learning algorithm based on the slowness principle and has originally been developed to learn invariances in a model of the primate visual system. Although developed for computational neuroscience, SFA has turned out to be a versatile algorithm also for technical applications since it can be used for feature extraction, dimensionality reduction, and invariance learning. With minor adaptations SFA can also be applied to supervised learning problems such as classification and regression. In this work, we review several illustrative examples of possible applications including the estimation of driving forces, nonlinear blind source separation, traffic sign recognition, and face processing. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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