Incremental Semiparametric Inverse Dynamics Learning

半参数半监督学习：在分布偏移和衰减重叠下的一般目标

Semiparametric semi-supervised learning for general targets under distribution shift and decaying overlap

本文提出D2S3框架，用于处理标签缺失随机且重叠衰减的半监督学习，实现高维噪声估计和分布偏移下的双鲁棒性和效率。

Efficient Inverse-Free Incremental and Decremental Algorithms for Multiple Hidden Nodes in Extreme Learning Machine

The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of a Hermitian matrix. Before that recursive algorithm was applied in [4], its improved version had been utilized in previous literatures [9], [10]. Accordingly from the improved recursive algorithm [9], [10], several efficient inverse-free algorithms for ELM were proposed in [13] to reduce the computational complexity. In this paper, we propose two inverse-fre…

Efficient Decremental Learning Algorithms for Broad Learning System

The decremented learning algorithms are required in machine learning, to prune redundant nodes and remove obsolete inline training samples. In this paper, an efficient decremented learning algorithm to prune redundant nodes is deduced from the incremental learning algorithm 1 proposed in [9] for added nodes, and two decremented learning algorithms to remove training samples are deduced from the two incremental learning algorithms proposed in [10] for added inputs. The proposed decremented learning algorithm for reduced nodes utilizes the inverse Cholesterol factor of the Herminia matrix in the…

Reducing the Computational Complexity of Pseudoinverse for the Incremental Broad Learning System on Added Inputs

In this brief, we improve the Broad Learning System (BLS) [7] by reducing the computational complexity of the incremental learning for added inputs. We utilize the inverse of a sum of matrices in [8] to improve a step in the pseudoinverse of a row-partitioned matrix. Accordingly we propose two fast algorithms for the cases of q > k and q < k, respectively, where q and k denote the number of additional training samples and the total number of nodes, respectively. Specifically, when q > k, the proposed algorithm computes only a k * k matrix inverse, instead of a q * q matrix inverse in the exist…

Two Efficient Ridge Solutions for the Incremental Broad Learning System on Added Inputs

This paper proposes the recursive and square-root BLS algorithms to improve the original BLS for new added inputs, which utilize the inverse and inverse Cholesky factor of the Hermitian matrix in the ridge inverse, respectively, to update the ridge solution. The recursive BLS updates the inverse by the matrix inversion lemma, while the square-root BLS updates the upper-triangular inverse Cholesky factor by multiplying it with an upper-triangular intermediate matrix. When the added p training samples are more than the total k nodes in the network, i.e., p>k, the inverse of a sum of matrices is …

增量式Broad Learning System新增节点下的两种岭回归解

Two Ridge Solutions for the Incremental Broad Learning System on Added Nodes

针对现有宽度学习系统（BLS）新增节点场景需假设岭参数趋近于0的局限，提出两种支持任意正岭参数的岭回归解，在保持复杂度相当的同时实现训练加速并提升测试准确率。