In order to efficiently reduce dimensionality in multi-view semi-supervised scenarios,this paper proposed semi-supervised canonical correlation analysis methods which used nonnegative low-rank graph to propagate labels. Global linear neighborhoods captured by nonnegative low-rank graph could utilize information from both direct and reachable indirect neighbors to preserve the global cluster structures,while the low-rank property retained a compressed representation of the graph. After estimating label infor...
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