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Classical manifold learning algorithm ISOMAP for data preprocessing, which discovers intrinsic data structures through geodesic distance computation on neighborhood graphs. The mapped low-dimensional representation enables further operations like classification and regression. Experimental results on software engineering datasets demonstrate ISOMAP's superior performance over PCA. The implementation includes key functions for neighborhood graph construction, shortest path calculation, and multidimensional scaling.

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