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Explaining and interpreting hyperdimensional computing classifiers on tabular data
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DOI：10.1016/j.neucom.2025.131643.png)
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Abstract
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• An explanation and an interpretation method are proposed for HDC on tabular data. • The methods are fast as they employ HDC’s efficient arithmetic vector operations. • The methods are faithful, validated with coherence checks and ablation studies. • The deletion and insertion metrics are adjusted to apply to the HDC classifier.
Keywords:
Hyperdimensional computing
Vector symbolic architectures
Model explanation
Model interpretation
Classification
Tabular data
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