[1]Mudasser NASEER,QIN Shi yin.Classification and recognition of image/nonimage data based on multinomial logistic regression with nonlinear dimensionality reduction[J].CAAI Transactions on Intelligent Systems,2010,5(1):85-93.
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Classification and recognition of image/nonimage data based on multinomial logistic regression with nonlinear dimensionality reduction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 1 Page number: 85-93 Column: 学术论文—机器感知与模式识别 Public date: 2010-02-25
Title:
Classification and recognition of image/nonimage data based on multinomial logistic regression with nonlinear dimensionality reduction
Author(s):
Mudasser NASEER;  QIN Shiyin
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100037, China
Keywords:
nonlinear dimensionality reduction;  data classification;  multinomial logistic regression; image/nonimage data
CLC:
TP391
DOI:
-
Abstract:
In pattern classification and recognition oriented to massively complex data most classifiers suffer from the curse of dimensionality. Manifold learning based nonlinear dimensionality reduction (NLDR) methods provide a good preprocessing to reduce dimensionality before applying any classification method on high dimensional data. Multinomial logistic regression (MLR) can be used to predict the class membership of feature data. In this study several unsupervised NLDR methods are employed to reduce dimensions of the data and the MLR is used for class prediction of image/nonimage data so that a new method of classification and recognition oriented to massively complex image/nonimage data is proposed based on multinomial Logistic regression with nonlinear dimensionality reduction. Through a series of experiments and comparative analysis with supervised NLDR methods for a lot of typical test data the new proposed method is validated to outperform other supervised NLDR ones.
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