自注意力机制视角下的偏最小二乘法 / PLS in the Mirror of Self-Attention
1️⃣ 一句话总结
本文发现偏最小二乘法(PLS)可以看作是一种线性化的自注意力机制,从而将PLS融入神经网络框架进行分析,同时指出PLS中的降维和预测因子选择特性,暗示了自注意力机制本身也包含一定程度的维度规范化,有助于提升学习效果。
This note provides an interesting observation on casting partial least square (PLS) as a linearized self-attention so that PLS may be studied within the neural network paradigm. On the other hand, the dimensionality reduction and selection of predictors in PLS may indicate that self-attention includes certain degree of dimensionality normalization toward improved learning.
自注意力机制视角下的偏最小二乘法 / PLS in the Mirror of Self-Attention
本文发现偏最小二乘法(PLS)可以看作是一种线性化的自注意力机制,从而将PLS融入神经网络框架进行分析,同时指出PLS中的降维和预测因子选择特性,暗示了自注意力机制本身也包含一定程度的维度规范化,有助于提升学习效果。
源自 arXiv: 2605.28592