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Tensor Decomposition of Biometric Data using Singular Value Decomposition

In Body Area Sensor Network (BAN) a huge amount of data is generated rapidly due to monitoring of chronic diseases suffered patients on a regular basis because they require immediate medical services. The communication between IoT-enabled devices generates heterogeneous data which is dimensionally large in nature. To reduce its multidimensional, existing state-of-the-art approaches are inadequate to handle it. It has been noticed from the literature that two mechanisms, Singular value decomposition (SVD) and Principal component analysis (PCA) are used in tensor decomposition. To address the aforementioned issues, in this paper, we have applied the Tensor Decomposition technique (TD), i.e., an SVD to reduce dimensionally of data. Moreover, we compare the performance of SVD on using a different dataset of varying size data. Form the comparative analysis, it has been observed that the data sets with more parameters in columns prone to less error compared to fewer parameters in the column with original datasets.