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<< /S /GoTo /D (section.1) >>
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(Introduction)
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<< /S /GoTo /D (section.2) >>
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(Fundamentals and Multilinear Projections)
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<< /S /GoTo /D (subsection.2.1) >>
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(Notations)
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<< /S /GoTo /D (subsection.2.2) >>
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(Basic Multilinear Algebra)
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<< /S /GoTo /D (subsection.2.3) >>
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(Multilinear Projections)
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<< /S /GoTo /D (subsection.2.4) >>
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(Relationships between Multilinear Projections)
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<< /S /GoTo /D (section.3) >>
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28 0 obj
(The Multilinear Subspace Learning Framework)
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<< /S /GoTo /D (subsection.3.1) >>
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(Linear Subspace Learning)
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<< /S /GoTo /D (subsection.3.2) >>
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36 0 obj
(Multilinear Subspace Learning)
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<< /S /GoTo /D (subsection.3.3) >>
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(Scatter Measures for Multilinear Subspace Learning)
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<< /S /GoTo /D (subsection.3.4) >>
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(Typical Approach and Algorithmic Issues)
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<< /S /GoTo /D (section.4) >>
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(Taxonomy of Multilinear Subspace Learning Algorithms)
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<< /S /GoTo /D (subsection.4.1) >>
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(Unsupervised Multilinear Subspace Learning Algorithms)
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<< /S /GoTo /D (subsection.4.2) >>
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(Supervised Multilinear Subspace Learning Algorithms)
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<< /S /GoTo /D (subsection.4.3) >>
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(The Historical Basis of MSL )
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<< /S /GoTo /D (section.5) >>
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(Multilinear Subspace Learning Applications)
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<< /S /GoTo /D (subsection.5.1) >>
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(Face Recognition and Gait Recognition)
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<< /S /GoTo /D (subsection.5.2) >>
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(Music Genre Classification)
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<< /S /GoTo /D (subsection.5.3) >>
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(EEG Signal Classification)
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<< /S /GoTo /D (subsection.5.4) >>
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(Anomaly Detection)
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<< /S /GoTo /D (subsection.5.5) >>
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(Visual Content Analysis)
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<< /S /GoTo /D (section.6) >>
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(Conclusions and Future Works)
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<< /S /GoTo /D (section*.2) >>
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(References)
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<< /S /GoTo /D [94 0 R /Fit ] >>
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