Open AccessSurvey on Image Dimensionality Reduction Using Deep Learning Techniques
Author(s) -
K. M. Monica,
G. R. Bindu,
S. Sridevi
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.27.17755
Subject(s) - computer science , scope (computer science) , dimensionality reduction , task (project management) , range (aeronautics) , artificial intelligence , image (mathematics) , process (computing) , set (abstract data type) , semantics (computer science) , deep learning , curse of dimensionality , data set , reduction (mathematics) , data mining , machine learning , dimension (graph theory) , mathematics , engineering , systems engineering , geometry , pure mathematics , programming language , operating system , aerospace engineering
Images provide rich information. With reference to the data set which may be related or unrelated in nature, locates step by step, a wide range of application and its attributes through capturing mechanism by sensing the suitable technologies. On the other hand, it also creates a huge quantity of data which may be relevant, irrelevant or redundant in nature and it is used for detailed task of the image. Also, Many brings a lot of problems such as increase in computational time of image, density of image and range of mapping of data, semantics of the data set and also it also there is a scope of huge amount of labeled data for the process of training to the new environment setup. Mostly, this is not easy and costly for users to obtain sufficient training models in several application modules. This research paper deals with these problems by exploring the more classical dimension reduction algorithms with deep knowledge for supporting communities.