Description: As a new unsupervised learning met hod , manifold learning is capt uring increasing interest s of re2
searchers in the field of machine learning and cognitive sciences. To under stand manifold learning bet ter ,
t he topology concept of manifold learning was presented firstly , and t hen it s development history was
t raced. Based on different representations of manifold , several major algorit hms were int roduced , whose
advantages and defect s were pointed out respectively. Af ter that , two kinds of typical applications of Iso2
map and LL E were indicated. The result s show that compared wit h t raditional linear method , manifold
learning can discover t he int rinsic dimensions of nonlinear high2dimensional data effectively , helping re2
searchers to reduce dimensionality and analyze data bet ter . Finally t he prospect of manifold learning was
discussed , so as to extend t he application area of manifold learning.
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