Publications (23)
Does generalization performance of regularization learning depend on ? A negative example
Shaobo Lin, Chen Xu, Jingshan Zeng +1
-regularization has been demonstrated to be an attractive technique in machine learning and statistical modeling. It attempts to improve the generalization (prediction) capabi…
Sparse Solution of Underdetermined Linear Equations via Adaptively Iterative Thresholding
Jinshan Zeng, Shaobo Lin, Zongben Xu
Finding the sparset solution of an underdetermined system of linear equations has attracted considerable attention in recent years. Among a large number of algorithms, itera…
Explore the Power of Synthetic Data on Few-shot Object Detection
Shaobo Lin, Kun Wang, Xingyu Zeng +1
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. The few training samples restrict the performance o…
An Effective Crop-Paste Pipeline for Few-shot Object Detection
Shaobo Lin, Kun Wang, Xingyu Zeng +1
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. However, detecting novel categories with only a few…
Model selection of polynomial kernel regression
Shaobo Lin, Xingping Sun, Zongben Xu +1
Polynomial kernel regression is one of the standard and state-of-the-art learning strategies. However, as is well known, the choices of the degree of polynomial kernel and the regu…
A Unified Framework with Meta-dropout for Few-shot Learning
Shaobo Lin, Xingyu Zeng, Rui Zhao
Conventional training of deep neural networks usually requires a substantial amount of data with expensive human annotations. In this paper, we utilize the idea of meta-learning to…
Sparse Regularization: Convergence Of Iterative Jumping Thresholding Algorithm
Jinshan Zeng, Shaobo Lin, Zongben Xu
In recent studies on sparse modeling, non-convex penalties have received considerable attentions due to their superiorities on sparsity-inducing over the convex counterparts. Compa…
Learning and approximation capability of orthogonal super greedy algorithm
Jian Fang, Shaobo Lin, Zongben Xu
We consider the approximation capability of orthogonal super greedy algorithms (OSGA) and its applications in supervised learning. OSGA is concerned with selecting more than one at…
Constructive neural network learning
Shaobo Lin, Jinshan Zeng, Xiaoqin Zhang
In this paper, we aim at developing scalable neural network-type learning systems. Motivated by the idea of "constructive neural networks" in approximation theory, we focus on "con…
Greedy metrics in orthogonal greedy learning
Lin Xu, Shaobo Lin, Jinshan Zeng +1
Orthogonal greedy learning (OGL) is a stepwise learning scheme that adds a new atom from a dictionary via the steepest gradient descent and build the estimator via orthogonal proje…
Shrinkage degree in -re-scale boosting for regression
Lin Xu, Shaobo Lin, Yao Wang +1
Re-scale boosting (RBoosting) is a variant of boosting which can essentially improve the generalization performance of boosting learning. The key feature of RBoosting lies in intro…
Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II)
Shaobo Lin, Xia Liu, Jian Fang +1
An extreme learning machine (ELM) can be regarded as a two stage feed-forward neural network (FNN) learning system which randomly assigns the connections with and within hidden neu…
Learning rates of coefficient regularization learning with Gaussian kernel
Shaobo Lin, Jinshan Zeng, Jian Fang +1
Regularization is a well recognized powerful strategy to improve the performance of a learning machine and regularization schemes with are central in use. It is…
Learning through deterministic assignment of hidden parameters
Jian Fang, Shaobo Lin, Zongben Xu
Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameter…
Greedy Criterion in Orthogonal Greedy Learning
Lin Xu, Shaobo Lin, Jinshan Zeng +2
Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts with selecting a new atom from a specified dictionary via the steepest gradient descent (SGD) and then bu…
Divide and Conquer Local Average Regression
Xiangyu Chang, Shaobo Lin, Yao Wang
The divide and conquer strategy, which breaks a massive data set into a se- ries of manageable data blocks, and then combines the independent results of data blocks to obtain a fin…
Re-scale boosting for regression and classification
Shaobo Lin, Yao Wang, Lin Xu
Boosting is a learning scheme that combines weak prediction rules to produce a strong composite estimator, with the underlying intuition that one can obtain accurate prediction rul…
A Cyclic Coordinate Descent Algorithm for lq Regularization
Jinshan Zeng, Zhimin Peng, Shaobo Lin +1
In recent studies on sparse modeling, () regularization has received considerable attention due to its superiorities on sparsity-inducing and bias reduction over the $…
Global Convergence of Block Coordinate Descent in Deep Learning
Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin +1
Deep learning has aroused extensive attention due to its great empirical success. The efficiency of the block coordinate descent (BCD) methods has been recently demonstrated in dee…
Explore the Power of Dropout on Few-shot Learning
Shaobo Lin, Xingyu Zeng, Rui Zhao
The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this pa…
Regularization: Convergence of Iterative Half Thresholding Algorithm
Jinshan Zeng, Shaobo Lin, Yao Wang +1
In recent studies on sparse modeling, the nonconvex regularization approaches (particularly, regularization with ) have been demonstrated to possess capability o…
A Gauss-Seidel Iterative Thresholding Algorithm for lq Regularized Least Squares Regression
Jinshan Zeng, Zhimin Peng, Shaobo Lin
In recent studies on sparse modeling, () regularized least squares regression (LS) has received considerable attention due to its superiorities on sparsity-induci…
Nonparametric regression using needlet kernels for spherical data
Shaobo Lin
Needlets have been recognized as state-of-the-art tools to tackle spherical data, due to their excellent localization properties in both spacial and frequency domains. This paper c…