In this paper, we prove that the principal component analysis (PCA) and the linear discriminant analysis (LDA) can be directly implemented in the discrete cosine transform (DCT) domain and the results are exactly the same as the one obtained from the spatial domain. In some applications, compressed images are desirable to reduce the storage requirement. For images compressed using the DCT, e.g....

a novel hybrid multifocus image fusion method. First, the source multifocus images are decomposed using the nonsubsampled contourlet transform (NSCT). The low-frequency sub-band coefficients are fused by the sum-modified-Laplacian-based local visual contrast, whereas the high-frequency sub-band coefficients are fused by the local Log-Gabor energy. The initial fused image is subsequently reconst...

This paper compares the performance of face image retrieval system based on discrete wavelet transforms and Lifting wavelet transforms with principal component analysis (PCA). These techniques are implemented and their performances are investigated using frontal facial images from the ORL database. The Discrete Wavelet Transform is effective in representing image features and is suitable in Fac...

In this paper, we propose a weighted principal component analysis (WPCA) using the result of fuzzy clustering [4]. The principal component analysis (PCA) [1], [7] is one widely used and well-known data analysis method. However there is a problem, when the data does not have a structure that the PCA can capture we cannot obtain any satisfactory results. For the most part, this is due to the unif...

background: municipal solid waste (msw) is the natural result of human activities. msw generation modeling is of prime im­portance in designing and programming municipal solid waste management system. this study tests the short-term pre­diction of waste generation by artificial neural network (ann) and principal component-regression analysis. methods: two forecasting techniques are presented in...

this paper presents a combination of data envelopment analysis (dea) and principal component analysis (pca) to reduce the dimensionality of data set. dea is known as effective tool for assessment and benchmarking. the weak point of dea, it is that the number of efficient dmus relies on the number of variables (inputs and outputs). for solving this, first, we do principal component analysis (pca...

This paper addresses the problem of resource allocation in local linear models for non-linear principal component analysis (PCA). In the local PCA model, the data space is partitioned into regions and PCA is performed in each region. Our primary result is that the advantage of these models over conventional PCA has been signiicantly underestimated in previous work. We apply local PCA models to ...