MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
Bo Zhao, Xinwei Sun, Yanwei Fu, Yuan Yao and Yizhou Wang
We propose the idea that the features consist of three orthogonal parts, namely sparse strong signals, dense weak signals and random noise, in which both strong and weak signals contribute to the fitting of data. To facilitate such novel decomposition, MSplit LBI is for the first time proposed to realize feature selection and dense estimation simultaneously. We provide theoretical and simulational verification that our method exceeds L1 and L2 regularization, and extensive experimental results show that our method achieves state-of-the-art performance in the few-shot and zero-shot learning.