<ul><li>sparseGeoHOPCA is a new framework for sparse higher-order principal component analysis (SHOPCA) that utilizes a geometric approach for tensor decomposition.</li><li>The method converts the original nonconvex sparse objective into a manageable geometric form by restructuring subproblems as structured binary linear optimization problems.</li><li>sparseGeoHOPCA eliminates the need for covariance estimation and iterative deflation, leading to improved computational efficiency and interpretability, especially in high-dimensional and unbalanced data scenarios.</li><li>The algorithm achieves a total computational complexity that scales linearly with tensor size and has been shown to accurately recover sparse supports, maintain classification performance under compression, and offer high-quality image reconstruction on ImageNet.</li></ul>

sparseGeoHOPCA: A Geometric Solution to Sparse Higher-Order PCA Without Covariance Estimation

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