A Fast Dimension Reduction Algorithm with Applications on Face Recognition and Text Classification
2003-12-19
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A Fast Dimension Reduction Algorithm with Applications on Face Recognition and Text Classification
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2003-12-19
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In undersampled problems where the number of samples is smaller than the dimension of data space, it is difficult to apply Linear Discriminant Analysis (LDA) due to the singularity of scatter matrices caused by high dimensionality. We propose a fast dimension reduction method based on a simple modification of Principal Component Analysis (PCA) and the orthogonal decomposition. The proposed algorithm is an efficient way to perform LDA for undersampled problems. Our experimental results in face recognition and text classification demonstrate the effectiveness of our proposed method.
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Park, Cheonghee; Park, Haesun. (2003). A Fast Dimension Reduction Algorithm with Applications on Face Recognition and Text Classification. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215593.
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