Online Talk

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Many scientific and engineering applications utilize a tensor format for data representations. However, memory and computational costs can be prohibitive due to the exponentially growing size of higher-order tensors. To address these barriers, we focus on both computational and theoretical aspects of tensor dimensionality reduction methods. We adopt classical reduction approaches such as random projections and principal component analysis (PCA), and pursue scalable algorithms tailored to solving tensor programs. We also discuss theoretical accuracy guarantees for reduced data.