An improved isometric feature mapping(ISOMAP)algorithm for classification task, called ISOMAP-C, is
proposed, which employs label information to guide the dimensionality reduction for high dimensional datasets. Firstly,
within-class neighborhood graphs are constructed over each sub dataset belonging to the same class according to label
information. Secondly, the between-class neighborhood edges with the shortest distance are searched for, which is multiplied
by scaling factor greater than one so that low dimensional dataset after mapping become more compact within class and more
separate between classes. Finally, the mapping function from original high dimensional space to low dimensional space can
be approximately modeled by using Back-Propagation neural network, whose initial weights and thresholds are optimized
by using genetic algorithms to avoid local minimum using gradient decent techniques. The experimental results show that
the performance of classification is greatly enhanced and the alsorithm has robust for noisy data.