Maximum Margin Projection Subspace Learning for Visual Data Analysis

Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2014-10, Vol.23 (10), p.4413-4425
Main Authors: Nikitidis, Symeon, Tefas, Anastasios, Pitas, Ioannis
Format: Article
Language:English
Subjects:
Algorithms
Classification
Object recognition
Optimization algorithms
Projection
Reduction
Subspaces
Visual
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called maximum margin projection pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated, i.e., are separated with maximum margin. The proposed method is an iterative alternate optimization algorithm that computes the maximum margin projections exploiting the separating hyperplanes obtained from training a support vector machine classifier in the identified low dimensional space. Experimental results on both artificial data, as well as, on popular databases for facial expression, face and object recognition verified the superiority of the proposed method against various state-of-the-art dimensionality reduction algorithms.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2014.2348868