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- W1480002967 abstract "Principal components analysis (PCA) (Alpaydin, 2010; Jolliffe, 2002) is an effective unsupervised feature extraction algorithm for pattern recognition, classification, computer vision or data compression (Bravo et al., 2010; Zhang et al., 2006; Kim et al., 2005; Liying & Weiwei, 2009; Pavan et al., 2007; Qian & James, 2008). The goal of PCA is to obtain a compact and accurate representation of the data that reduces or eliminates statistically redundant components. Basic approaches for PCA involve the computation of the covariance matrix and the extraction of eigenvalues and eigenvectors. A drawback of the basic approaches is the high computational complexity and large memory requirement for data with high vector dimension. Therefore, these approaches may not be well suited for real time applications requiring fast feature extraction." @default.
- W1480002967 created "2016-06-24" @default.
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- W1480002967 date "2012-03-02" @default.
- W1480002967 modified "2023-09-26" @default.
- W1480002967 title "FPGA Implementation for GHA-Based Texture Classification" @default.
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- W1480002967 doi "https://doi.org/10.5772/37987" @default.
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