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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| Published in: | IEEE transactions on pattern analysis and machine intelligence Vol. 33; no. 6; pp. 1147 - 1160 |
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| Main Authors: | , , |
| 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: | |
| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Tyng-Luh Liu Chiou-Shann Fuh Yen-Yu Lin |
| Author_xml | – sequence: 1 givenname: Yen-Yu surname: LIN fullname: LIN, Yen-Yu organization: Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, Province of China – sequence: 2 givenname: Tyng-Luh surname: LIU fullname: LIU, Tyng-Luh organization: Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, Province of China – sequence: 3 givenname: Chiou-Shann surname: FUH fullname: FUH, Chiou-Shann organization: Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Province of China |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | Multiple Kernel Learning for Dimensionality Reduction |
| URI | https://ieeexplore.ieee.org/document/5601738 https://www.ncbi.nlm.nih.gov/pubmed/20921580 https://www.proquest.com/docview/862907730 https://www.proquest.com/docview/870292445 https://www.proquest.com/docview/875044145 |
| Volume | 33 |
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