Principal component analysis (PCA) is one of the proposed methods to reduce the size of the data set that can be used for both one and two-dimensional data. Regarding the lack of sparsity property in the base vectors, sparse PCA has been proposed, which maintains the properties of standard PCA and simultaneously forces some of the elements of the base vectors to zero. In this paper, due to the sparsity in base vectors that cause some dataset values to be ineffective in moving to new space, two algorithms are presented in one-dimensional and two-dimensional mode to remove redundancy from raw data. In the one-dimensional algorithm, redundancy is detected between signal layers and then removed from all set observations. In a two-dimensional algorithm, the significance of the row and the column of the dataset images are detected and the less important ones are eliminated directly from raw data. One of the most important advantages of proposed algorithms, which can be read as non-uniform sampling methods, is to preserve the appearance of signals. After removing the raw data redundancy by the two algorithms presented, new data with fewer dimensions can be used in other applications such as dataset recognition, compression, and so on.