Online Coregularization for Multiview Semisupervised Learning

Table 3

Mean test error rates on the web page data set. The error rates are reported for three different sparse approximations. For gradient ascent, we choose a decaying step size 𝜌_𝑡=0.1/√𝑡. The result shows that our derived online co-regularization algorithms achieve test accuracy comparable to offline co-regularization (CoLapSVM).

Online co-regularization algorithms	Gradient ascent	ADA 𝑆𝑡={𝑡}	ADA 𝑆𝑡=[𝑡]	ADA Buffer-𝑁	ADA Buffer-𝐿
Error rates (%)
No sparse approximation	10.94	11.89	7.52	8.47	7.80
Absolute threshold (𝜀=0.05)	11.23	11.70	7.52	8.66	7.80
𝑘-MC (𝑘=400)	11.32	11.41	7.80	8.75	7.99