چکیده مقاله
In the recent years, because technology has improved rapidly, the size of data such as digital photographs becomes very high For time and computation efficiency, we need to extract some features from this high dimensional data Hence, in this paper, a new dimensionality reduction algorithm is proposed to extract features for the classification purpose The proposed method is based on graph embedding which is a general framework for describing many dimensional reduction methods In this framework, similarity and penalty graphs are constructed based on data relations These graphs characterize the statistical or geometric property of the data that should be kept or avoided during dimensionality reduction Our proposed method constructs these two graphs on data and uses the averaging idea among neighbor vertices of the graphs to imply the compactness in each class of data while separating different classes Obtained results show that the proposed method improves the accuracy of classification task on data such as face and digit images
کلیدواژهها
نویسندگان
Mohammad Amin Naeemi
Department of Computer, Shahid Bahonar University, Kerman, Iran
Hadis Mohseni
Young researchers group, Shahid Bahonar University, Kerman, Iran
شیوه ارجاع
Naeemi, Mohammad Amin and Mohseni, Hadis,1395,Graph Based Classification Using kNN Averaging and Graph Embedding Criterion,4th National Congress of Electrical and Computer Engineering of Iran,Tehran
ارائهشده در
مجموعه مقالات چهارمین کنفرانس بین المللی مهندسی برق و کامپیوتر23 دی 1395 · تهران