The subspace method in Hilbert space
The subspace method has usually been applied to a multidimensional space (i.e., feature space) which uses features as its basis. A subspace method can also be applied to a functional space, since the subspace can be defined by an arbitrary linear space. This paper proposes the mapping of a feature space onto the Hilbert subspace so that pattern recognition can be performed by the subspace method. The proposed method has been experimentally applied to the recognition of the Japanese hiragana syllabary and has achieved a higher recognition rate than the conventional method. This improvement is based on the fact that the dimension of the common part in all of the classes of the subspaces in the Hilbert function space becomes zero. © 2001 Scripta Technica, Syst Comp Jpn, 32(6): 55–61, 2001