Robust Sparse Representation for Incomplete and Noisy Data
- Release time:2024-08-09
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Affiliation of Author(s):
理学院
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Journal:
Information
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Key Words:
中文关键字：稀疏表示，英文关键字：sparse representation; robust; face classification
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Abstract:
Owing to the robustness of large sparse corruptions and the discrimination of
class labels, sparse signal representation has been one of the most advanced techniques in
the fields of pattern classification, computer vision, machine learning and so on. This paper
investigates the problem of robust face classification when a test sample has missing
values. Firstly, we propose a classification method based on the incomplete sparse
representation. This representation is boiled down to an l 1 minimization problem and an
alternating direction method of multipliers is employed to solve it. Then, we provide a
convergent analysis and a model extension on incomplete sparse representation. Finally,
we conduct experiments on two real-world face datasets and compare the proposed method
with the nearest neighbor classifier and the sparse representation-based classification. The
experimental results demonstrate that the proposed method has the superiority in
classification accuracy, completion of the missing entries and recovery of noise.
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Note:
EI
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First Author:
yangwei,zhengxiuyun,shijiarong
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Indexed by:
Journal paper
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Volume:
卷：6
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Issue:
期：3
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Page Number:
页：287-299
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Translation or Not:
no
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Date of Publication:
2015-06-01
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