Recognizing MNIST Handwritten Data Set Using PCA and LDA
| Autor: | Mayank Patel, Ruksar Sheikh, Amit Sinhal |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Dimensionality reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Linear discriminant analysis Set (abstract data type) Data set ComputingMethodologies_PATTERNRECOGNITION Data visualization Dimension (vector space) Computer Science::Computer Vision and Pattern Recognition Principal component analysis Artificial intelligence business MNIST database |
| Zdroj: | Algorithms for Intelligent Systems ISBN: 9789811510588 |
| DOI: | 10.1007/978-981-15-1059-5_20 |
| Popis: | In data analyzing and machine learning, PCA and LDA are widely used tools, these are linear transformation methods to reduce the dimension observation, and these are used in many practical applications like compression and data visualization in machine learning. Here, I have applied these two methods that are principal component analysis (PCA) and linear discriminant analysis (LDA) in handwritten digit recognition. The main motto of this paper is to present a simple and clear understanding of these methods. Here, this paper depicts that LDA can outperform PCA when training data set is huge, and PCA can outperform LDA when training data set is small. |
| Databáze: | OpenAIRE |
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