Conference Paper

Feature Extraction for Regression Problems and an Example Application for Pose Estimation of a Face.

DOI: 10.1007/978-3-540-69812-8_43 In proceeding of: Image Analysis and Recognition, 5th International Conference, ICIAR 2008, Póvoa de Varzim, Portugal, June 25-27, 2008. Proceedings
Source: DBLP

ABSTRACT In this paper, we propose a new feature extraction method for regression problems. It is a modified version of linear discriminant
analysis (LDA) which is a very successful feature extraction method for classification problems. In the proposed method, the
between class and the within class scatter matrices in LDA are modified so that they fit in regression problems. The samples
with small differences in the target values are used to constitute the within class scatter matrix while the ones with large
differences in the target values are used for the between class scatter matrix. We have applied the proposed method in estimating
the head pose and compared the performance with the conventional feature extraction methods.

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    ABSTRACT: In the Fall of 2000, we collected a database of more than 40,000 facial images of 68 people. Using the Carnegie Mellon University 3D Room, we imaged each person across 13 different poses, under 43 different illumination conditions, and with four different expressions. We call this the CMU pose, illumination, and expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database.
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    ABSTRACT: In manipulating data such as in supervised learning, we often extract new features from the original features for the purpose of reducing the dimensions of feature space and achieving better performance. In this paper, we show how standard algorithms for independent component analysis (ICA) can be applied to extract features for regression problems. The advantage is that general ICA algorithms become available to a task of feature extraction for regression problems by maximizing the joint mutual information between target variable and new features. Using the new features, we can greatly reduce the dimension of feature space without degrading the regression performance.
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