THESIS
2020
1 online resource (xi, 52 pages) : color illustrations
Abstract
Multi-label learning is an important problem which is ubiquitous in real world
computer vision and machine learning applications, where one instance can be assigned
to multiple classes simultaneously. In this thesis, we introduce the problem
of multi-label learning with a variety of challenges such as class correlations and
class imbalance, and provide an improvement of a recent algorithm. Among many
algorithms, Baoyuan Wu et al. [AAAI, pp:2229–2236, 2016] recently formulate
multi-label learning as a constrained submodular minimization, whose objective
function encourages label consistency and smoothness via graph Laplacians and
whose class cardinality bounds handles the class imbalance issue. Its convex relaxation
is a convex quadratic programming problem, and can thus be solved effect...[
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Multi-label learning is an important problem which is ubiquitous in real world
computer vision and machine learning applications, where one instance can be assigned
to multiple classes simultaneously. In this thesis, we introduce the problem
of multi-label learning with a variety of challenges such as class correlations and
class imbalance, and provide an improvement of a recent algorithm. Among many
algorithms, Baoyuan Wu et al. [AAAI, pp:2229–2236, 2016] recently formulate
multi-label learning as a constrained submodular minimization, whose objective
function encourages label consistency and smoothness via graph Laplacians and
whose class cardinality bounds handles the class imbalance issue. Its convex relaxation
is a convex quadratic programming problem, and can thus be solved effectively
by the alternative direction method of multipliers (ADMM). However, in
practice such a convex approach leads to degenerated prediction where all entries
of predicted values are pushed to positive or negative simultaneously. To overcome
this issue, we propose new linear constraints such that predictions will be orthogonal
to constant vectors to eliminate degenerated predictions. Moreover, we replace
uniform graph Laplacian by class-specific one to make label smoothness more accurate,
and further improve class cardinality bounds by making class imbalance
bounds adaptive to each instance and introducing new structured bounds via independent
component analysis. Although the new optimization problem becomes
nonconvex, we find a heuristic iterative algorithm inspired by ADMM effective
in practice. The experiments over three multi-label classification datasets (yeast,
emotions and scene) show that the proposed method with new structured bounds
effectively reduces the degenerated cases and improves robustness with respect to
parameters of model. On the other hand, class-specific graph Laplacian does not
improve the performance, and the non-convexity in the optimization problem may
cause failure of algorithmic convergence that needs future investigation.
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