کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6939228 1449969 2018 35 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Parameterized principal component analysis
ترجمه فارسی عنوان
تجزیه و تحلیل مولفه های پارامتریک شده
کلمات کلیدی
یادگیری منیفولد، تقریب منیفولد، مدل سازی چهره، تجزیه و تحلیل مولفه اصلی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
When modeling multivariate data, one might have an extra parameter of contextual information that could be used to treat some observations as more similar to others. For example, images of faces can vary by age, and one would expect the face of a 40 year old to be more similar to the face of a 30 year old than to a baby face. We introduce a novel manifold approximation method, parameterized principal component analysis (PPCA) that models data with linear subspaces that change continuously according to the extra parameter of contextual information (e.g. age), instead of ad-hoc atlases. Special care has been taken in the loss function and the optimization method to encourage smoothly changing subspaces across the parameter values. The approach ensures that each observation's projection will share information with observations that have similar parameter values, but not with observations that have large parameter differences. We tested PPCA on artificial data based on known, smooth functions of an added parameter, as well as on two real datasets with different types of parameters. We compared PPCA to PCA, sparse PCA and to independent principal component analysis (IPCA), an atlas based method that groups observations by their parameter values and projects each group using PCA with no sharing of information between groups. PPCA recovers the known functions with less error and projects the datasets' test set observations with consistently less reconstruction error than IPCA does. In some cases where the manifold is truly nonlinear, PCA outperforms all the other manifold approximation methods compared.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 78, June 2018, Pages 215-227
نویسندگان
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