{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T05:23:42Z","timestamp":1698125022370},"reference-count":12,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2007,3,21]],"date-time":"2007-03-21T00:00:00Z","timestamp":1174435200000},"content-version":"vor","delay-in-days":5923,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems &amp; Computers in Japan"],"published-print":{"date-parts":[[1991,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper proposes a method for concentrating multivariate data (dimensionality reduction) using a neural network model. Specifically, a pulse\u2010input pattern\u2010output network (PPN), i.e., a multilayer network in which the input is presented as a pulsed input (a single true input with the others being held to zero Here, by pulsed input, we mean that only one component of the input vector is presented with a value of 1 and has no connection with pulsed signals in the time domain.), is employed and the <jats:italic>N<\/jats:italic>\u2010dimensional training pattern is presented at the outputs. Using backpropagation learning in a PPN, it is demonstrated mathematically that subject to a certain set of conditions, the dimension of the sample space can be reduced arbitrarily while preserving optimality. That capability is compared with principal component analysis (K\u2010L expansion), and it is demonstrated that the nonlinear dimensionality reduction capabilities of the model by means of a component analysis experiment employ the iris data\u2014a data set which is well known in the field of multivariate analysis.<\/jats:p>","DOI":"10.1002\/scj.4690221105","type":"journal-article","created":{"date-parts":[[2007,7,7]],"date-time":"2007-07-07T20:03:41Z","timestamp":1183838621000},"page":"42-51","source":"Crossref","is-referenced-by-count":0,"title":["A Method of Information Integration by Multilayer Neural Networks and Its Theoretical Study"],"prefix":"10.1002","volume":"22","author":[{"given":"Tatsuhiro","family":"Yonekura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigeki","family":"Yokoi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun\u2010Ichiro","family":"Toriwaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2007,3,21]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/5236.001.0001"},{"key":"e_1_2_1_3_2","unstructured":"J. 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Manufacturing Library (1981)."},{"key":"e_1_2_1_10_2","unstructured":"TatsuhiroYonekura ShigekiYokoi andJun\u2010ichirooToriwaki.Image representation by PRIMITIVE transform of grey images using neural networks. Proceedings of the Symposium on Improving and Accelerating Image Understanding. I.E.I.C.E. Japan pp.127\u2013132(1989)."},{"key":"e_1_2_1_11_2","volume-title":"An Introduction to Multivariate Statistical Analysis, 2nd Edition","author":"Anderson T. W.","year":"1984"},{"key":"e_1_2_1_12_2","doi-asserted-by":"crossref","unstructured":"S.Usui S.Nakauchi andM.Nakano.Reconstruction of Munsell color space of a five\u2010layered neural network. IJCNN'90 (San Diego) II pp.515\u2013520(1990).","DOI":"10.1109\/IJCNN.1990.137762"},{"key":"e_1_2_1_13_2","unstructured":"YasuoKatayamaandKoichiOhyama.Some characteristics of self\u2010organizing backpropagation neural networks. Spring National Convention Record I.E.I.C.E. 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