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arXiv · 2406.13762

Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis

Rachel S. Y. Teo·Tan M. Nguyen
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Abstract

The remarkable success of transformers in sequence modeling tasks, spanning various applications in natural language processing and computer vision, is attributed to the critical role of self-attention. Similar to the development of most deep learning models, the construction of these attention mechanisms relies on heuristics and experience. In our work, we derive self-attention from kernel principal component analysis (kernel PCA) and show that self-attention projects its query vectors onto the principal component axes of its key matrix in a feature space. We then formulate the exact formula for the value matrix in self-attention, theoretically and empirically demonstrating that this value matrix captures the eigenvectors of the Gram matrix of the key vectors in self-attention. Leveraging our kernel PCA framework, we propose Attention with Robust Principal Components (RPC-Attention), a novel class of robust attention that is resilient to data contamination. We empirically demonstrate the advantages of RPC-Attention over softmax attention on the ImageNet-1K object classification, WikiText-103 language modeling, and ADE20K image segmentation task.

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Rachel S. Y. Teo, Tan M. Nguyen. 2024-06-19. Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis. https://arxiv.org/abs/2406.13762

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