时间：2023-08-04
本文（Efficient Sparse Representation for Learning with High-dimensional Data）原载IEEE Transactions on Neural Networks and Learning Syetems，由四川大学王竹教授等科研人员创作，系四川大学市场经济法治研究所系列学术成果。后续会持续分享四川大学市场经济法治研究所系列学术成果，欢迎大家阅读。
Due to the capability of effectively learning intrinsic
structures from high-dimensional data, techniques based on
sparse representation have begun to display an impressive impact
on several fields, such as image processing, computer vision, and
pattern recognition. Learning sparse representations isoften com
putationally expensive due to the iterative computations needed
to solve convex optimization problems in which the number
of iterations is unknown before convergence. Moreover, most
sparse representation algorithms focus only on determining the
final sparse representation results and ignore the changes in the
sparsity ratio (SR) during iterative computations. In this article,
two algorithms are proposed to learn sparse representations
based on locality-constrained linear representation learning with
probabilistic simplex constraints. Specifically, the first algorithm,
called approximated local linear representation (ALLR), obtains
a closed-form solution from individual locality-constrained sparse
representations. The second algorithm, called ALLR with sym
metric constraints (ALLRSC), further obtains a symmetric sparse
representation result with a limited number of computations;
notably, the sparsity and convergence of sparse representations
can be guaranteed based on theoretical analysis. The steady
decline in the SR during iterative computations is a critical factor
in practical applications. Experimental results based on public
datasets demonstrate that the proposed algorithms perform
better than several state-of-the-art algorithms for learning with
high-dimensional data.
Jie Chen, Shengxiang Yang, Zhu Wang, and Hua Mao. Efficient Sparse Representation for Learning with High-dimensional Data. IEEE Transactions on Neural Networks and Learning Syetems, Vol. 34, No. 8, August 2023.（论文下载）
