Abstract
This paper develops a novel and efficient dimension reduction scheme–Fast Adaptive Discriminant Analysis (FADA). FADA can find a good projection with adaptation to different sample distributions and discover the classification in the subspace with naïve Bayes classifier. FADA overcomes the high computational cost problem of current Adaptive Discriminant Analysis (ADA) and also alleviates the overfitting problem implicitly caused by ADA. FADA is tested and evaluated using synthetic dataset, COREL dataset and three different face datasets. The experimental results show FADA is more effective and computationally more efficient than ADA for image classification.
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Lu, Y., Ma, J., Tian, Q. (2007). FADA: An Efficient Dimension Reduction Scheme for Image Classification. In: Ip, H.HS., Au, O.C., Leung, H., Sun, MT., Ma, WY., Hu, SM. (eds) Advances in Multimedia Information Processing – PCM 2007. PCM 2007. Lecture Notes in Computer Science, vol 4810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77255-2_1
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DOI: https://doi.org/10.1007/978-3-540-77255-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77254-5
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