4 papers
Structural Loss Metrics for Tensor Approximation via Matrix Low-Rank Approximation
Hiroki Hasegawa
Matricized low-rank approximation via SVD is a standard surrogate for tensor decompositions, but entry-wise reconstruction error fails to capture multiway geometric degradation. Un…
Covariance-Based Structural Equation Modeling in Small-Sample Settings with
Hiroki Hasegawa, Aoba Tamura, Yukihiko Okada
Factor-based Structural Equation Modeling (SEM) relies on likelihood-based estimation assuming a nonsingular sample covariance matrix, which breaks down in small-sample settings wi…
Interaction Tensor SHAP
Hiroki Hasegawa, Yukihiko Okada
This study proposes Interaction Tensor SHAP (IT-SHAP), a tensor algebraic formulation of the Shapley Taylor Interaction Index (STII) that makes its computational structure explicit…
A Robust and Non-Iterative Tensor Decomposition Method with Automatic Thresholding
Hiroki Hasegawa, Yukihiko Okada
Recent advances in IoT and biometric sensing technologies have led to the generation of massive and high-dimensional tensor data, yet achieving accurate and efficient low-rank appr…