Web　data,　such　as　web　pages　and　web　images,　can　be　naturally　partitioned　into　multiple　heterogeneous　attribute　sets.　Concretely　speaking,　web　pages　consist　of　hyperlink　and　contents,　and　web　images　consist　of　the　textual　and　visual　information.　In　this　paper,　we　propose　a　new　multi-view　semi-supervised　learning　method,　named　local　co-training,　for　web　page　and　image　classification.　Local　co-training　employs　local　linear　models　to　represent　data　points　on　each　view　(i.e.　one　attribute　set),　and　iteratively　refines　them　using　unlabelled　data　with　co-training　strategy.　In　each　iteration,　only　a　part　of　local　models　that　we　call　dominant　local　models　needs　to　be　incrementally　updated.　The　method　is　thus　efficient　and　fit　for　the　learning　of　large-scale　web　data.　In　addition,　we　introduce　a　new　measurement　based　on　both　the　confidence　and　the　disagreement　to　describe　which　unlabelled　examples　are　＇good＇　for　the　enrichment　of　training　sets.　Local　co-training　builds　a　bridge　between　two　dominant　types　of　semi-supervised　methods:　graph-based　methods　and　co-training.　Experiments　on　web　page　and　web　image　datasets　demonstrate　that　local　co-training　can　effectively　improve　the　classification　performance　by　exploiting　multiple　attribute　sets　and　unlabelled　data.