PCA-based Multi Task Learning: a Random Matrix Approach
This work addresses computational efficiency in multi-task learning for researchers and practitioners, though it is incremental as it extends existing PCA-based schemes.
The paper tackles the problem of negative transfer in multi-task learning by proposing a computationally efficient PCA-based method, showing that simple label adjustments can prevent performance degradation and achieve comparable results to state-of-the-art methods with significantly reduced computational cost.
The article proposes and theoretically analyses a \emph{computationally efficient} multi-task learning (MTL) extension of popular principal component analysis (PCA)-based supervised learning schemes \cite{barshan2011supervised,bair2006prediction}. The analysis reveals that (i) by default learning may dramatically fail by suffering from \emph{negative transfer}, but that (ii) simple counter-measures on data labels avert negative transfer and necessarily result in improved performances. Supporting experiments on synthetic and real data benchmarks show that the proposed method achieves comparable performance with state-of-the-art MTL methods but at a \emph{significantly reduced computational cost}.