@article { author = {Saraswat, Monika and Wadhwani, A. K. and Dubey, Manish}, title = {Compression of Breast Cancer Images By Principal Component Analysis}, journal = {International Journal of Advanced Biological and Biomedical Research}, volume = {1}, number = {7}, pages = {767-776}, year = {2013}, publisher = {Sami Publishing Company}, issn = {2383-2762}, eissn = {2322-4827}, doi = {}, abstract = {The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN  of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most      relevant information of X. These eigenvectors are called principal components [8]. Assume that n images in a set are originally represented in matrix form as Ui∈ Rr ×c,  i = 1,......,n, where r and c are, repetitively, the number of rows and columns of the matrix. In vectorized representation (matrix-to-vector alignment) each Ui is a N = r × c- dimensional vector ai computed by sequentially concatenating all of the lines of the matrix Ui. To compute the Principal Components the covariance matrix of U is formed and Eigen values, with the corresponding eigenvectors, are evaluated. The Eigen vectors forms a set of linearly independent vectors, i.e., the base {φ} n i=1 which consist of a new axis system [10]}, keywords = {SNR,MSE,PSNR,Mammograms,PCA}, url = {https://www.ijabbr.com/article_7803.html}, eprint = {https://www.ijabbr.com/article_7803_c1bfed921b1cb6eddb3d3d9a00f75143.pdf} }