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New ROTOT method tackles outliers in tensor regression

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new robust tensor-on-tensor regression method called ROTOT, designed to handle outliers in both response and predictor tensors, as well as missing values. This method utilizes a single loss function to mitigate the impact of casewise and cellwise outliers in the response data. Outliers within the predictor tensors are addressed through a robust Multilinear Principal Component Analysis technique. The effectiveness of ROTOT was demonstrated through extensive simulations and an application to the Labeled Faces in the Wild dataset for predicting facial attributes. AI

IMPACT This research introduces a novel statistical method for analyzing tensor data, which could have implications for machine learning tasks involving complex, multi-dimensional datasets.

RANK_REASON This is a research paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ROTOT method tackles outliers in tensor regression

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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Mehdi Hirari, Fabio Centofanti, Mia Hubert, Stefan Van Aelst ·

    Casewise and Cellwise Robust Tensor-on-Tensor Regression

    arXiv:2603.25911v2 Announce Type: replace-cross Abstract: Tensor-on-tensor regression is an important tool for the analysis of tensor data, aiming to predict a set of response tensors from a corresponding set of predictor tensors. However, standard tensor-on-tensor regression is …

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