Multiple Kernel Learning for Dimensionality Reduction

In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them int...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 33; no. 6; pp. 1147 - 1160
Main Authors:	LIN, Yen-Yu, LIU, Tyng-Luh, FUH, Chiou-Shann
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.06.2011 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Applied sciences Artificial Intelligence Cluster Analysis Computer science; control theory; systems Data processing. List processing. Character string processing Dimensionality reduction Eigenvalues and eigenfunctions Exact sciences and technology Face - anatomy & histology face recognition Focusing Humans image clustering Image Processing, Computer-Assisted - methods Kernel Kernels Laplace equations Learning Learning and adaptive systems Machine learning Memory organisation. Data processing multiple kernel learning object categorization Optimization Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Principal component analysis Reduction Representations Software Tasks Training Visual Cluster analysis Dimensionality reduction Computer vision Image processing Face recognition Cluster Pattern recognition Object recognition image clustering Kernel method object categorization Dimension reduction Image analysis Multidimensional analysis multiple kernel learning Supervised learning Facies Learning algorithm Categorization Visual task
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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Summary:	In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting data representations are typically high-dimensional and assume diverse forms. Hence, finding a way of transforming them into a unified space of lower dimension generally facilitates the underlying tasks such as object recognition or clustering. To this end, the proposed approach (termed MKL-DR) generalizes the framework of multiple kernel learning for dimensionality reduction, and distinguishes itself with the following three main contributions: First, our method provides the convenience of using diverse image descriptors to describe useful characteristics of various aspects about the underlying data. Second, it extends a broad set of existing dimensionality reduction techniques to consider multiple kernel learning, and consequently improves their effectiveness. Third, by focusing on the techniques pertaining to dimensionality reduction, the formulation introduces a new class of applications with the multiple kernel learning framework to address not only the supervised learning problems but also the unsupervised and semi-supervised ones.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	0162-8828 1939-3539 2160-9292 1939-3539
DOI:	10.1109/TPAMI.2010.183