کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
533555 870133 2011 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds
چکیده انگلیسی

We propose “supervised principal component analysis (supervised PCA)”, a generalization of PCA that is uniquely effective for regression and classification problems with high-dimensional input data. It works by estimating a sequence of principal components that have maximal dependence on the response variable. The proposed supervised PCA is solvable in closed-form, and has a dual formulation that significantly reduces the computational complexity of problems in which the number of predictors greatly exceeds the number of observations (such as DNA microarray experiments). Furthermore, we show how the algorithm can be kernelized, which makes it applicable to non-linear dimensionality reduction tasks. Experimental results on various visualization, classification and regression problems show significant improvement over other supervised approaches both in accuracy and computational efficiency.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 44, Issue 7, July 2011, Pages 1357–1371
نویسندگان
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