no code implementations • 24 Nov 2020 • YuHan Wang, Zijian Lei, Liang Lan
This data-oblivious matrix sketching method could produce a bad sketched matrix which will result in low accuracy for subsequent machine learning tasks (e. g. classification); (2) For highly sparse input data, count-sketch could produce a dense sketched data matrix.
no code implementations • 6 Oct 2020 • Zijian Lei, Liang Lan
The analysis shows that the convergence to a local optimum is guaranteed, and the inference complexity of our model is much lower than other competing methods.
no code implementations • 5 Feb 2020 • Zijian Lei, Liang Lan
To overcome this limitation, we analyze the effect of using SRHT for random projection in the context of linear SVM classification.