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In our LQLE, each high-dimensional data vector is first expanded by using Kronecker product. The expanded vector contains not only the components of the original vector, but also the polynomials of its components. Then, each expanded vector of high dimensional data is linearly approximated with the expanded vectors of its nearest neighbors. In this way, the proposed LQLE achieves a certain degree of local nonlinearity and learns the data dimensionality reduction results under the principle of keeping local nonlinearity unchanged. More importantly, LQLE does not increase computation complexity by only replacing the data vectors with their Kronecker product expansions in the original LLE program. Experimental results between our proposed methods and four comparison algorithms on various datasets demonstrate the well performance of the proposed methods.<\/jats:p>","DOI":"10.3233\/jifs-210891","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T14:39:30Z","timestamp":1628606370000},"page":"2195-2205","source":"Crossref","is-referenced-by-count":1,"title":["Local quasi-linear embedding based on kronecker product expansion of vectors"],"prefix":"10.1177","volume":"41","author":[{"given":"Guo","family":"Niu","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Foshan University, Foshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengming","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Technology, SunYat-sen University, Guangzhou, 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