We can reduce n dimensions of dataset into k dimensions (where k < n).
The dimensions of dataset two were with an isotropic voxel size of.
However, these studies, usually, do not take into account temporal and social dimensions of the datasets and preferences of each user in a social group.
However, discovering SSTILs is challenging due to spatial, temporal, and social dimensions of the datasets, the lack of sufficient interest measures, and the need for developing computationally-efficient algorithms.
However, discovering socially important locations from a social network is challenging due to the data size and variety, spatial and temporal dimensions of the datasets, the need for developing computationally efficient approaches, and the difficulty of modeling human behavior.
The results depict that the LHNFCSF addresses the medical big data issues by reducing the dimensions of large datasets and speeding up the learning process and improves the classification performance.
Although microarray analysis tool can be used as an initial step to extract most relevant features, one has to avoid over-fitting the data and deal with the very large number of dimensions of the datasets.
Dimensions of the datasets were 400 (z, depth) × 300 (x, length) pixels over 300 (y, width) slices, distributed over a 2.7 (z, refractive index = 1.4 for cartilage) × 12 (x) × 12 (y) mm scanned area, resulting in a digital sampling resolution of 10 × 40 × 40 µm, equivalent to the optical resolution of the system.
Among the statistical techniques, both principal component analysis (PCA) and cluster analysis (CA) are useful methods to discover common patterns in data distribution, leading to initial dimension reduction of datasets and helping its interpretation (Franco-Uría et al. 2009).
In order to decrease the dimensions of input dataset by summarizing their most important modes of variability and correlations to the reference dataset, a technique named singular value decomposition (SVD) is used.
