no code implementations • 19 Nov 2024 • Ziheng Sun, Chris Ding, Jicong Fan
Feature selection is important for high-dimensional data analysis and is non-trivial in unsupervised learning problems such as dimensionality reduction and clustering.
no code implementations • 23 Apr 2024 • Chaohao Yang, Chris Ding
We propose two novel methods, Learnable Formulated Weights (LFW) and Epoch-based Dynamic Window Size (EDWS), to incorporate distance information into two variants of Word2Vec, the Continuous Bag-of-Words (CBOW) model and the Continuous Skip-gram (Skip-gram) model.
1 code implementation • 13 Aug 2023 • Wenwen Min, Taosheng Xu, Chris Ding
However, sPLS extracts the combinations between two data sets with all data samples so that it cannot detect latent subsets of samples.
1 code implementation • 18 May 2023 • Yixiong Chen, Li Liu, Chris Ding
This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation methods by generating textual explanations.
no code implementations • 1 Dec 2022 • Yixiong Chen, Jingxian Li, Chris Ding, Li Liu
Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans.
1 code implementation • 31 Oct 2022 • Longkang Li, Siyuan Liang, Zihao Zhu, Chris Ding, Hongyuan Zha, Baoyuan Wu
Compared to the state-of-the-art reinforcement learning method, our model's network parameters are reduced to only 37\% of theirs, and the solution gap of our model towards the expert solutions decreases from 6. 8\% to 1. 3\% on average.
1 code implementation • 3 Jun 2022 • Yixiong Chen, Li Liu, Jingxian Li, Hua Jiang, Chris Ding, Zongwei Zhou
In this work, we propose a meta-learning-based LR tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains.
no code implementations • 29 Sep 2021 • Jicong Fan, Rui Chen, Chris Ding
We provide theoretical analysis for NE-AECF to investigate the generalization ability of autoencoder and deep learning in collaborative filtering.
no code implementations • 22 Nov 2020 • Runkai Zheng, Zhijia Yu, Yinqi Zhang, Chris Ding, Hei Victor Cheng, Li Liu
A major challenge in Fine-Grained Visual Classification (FGVC) is distinguishing various categories with high inter-class similarity by learning the feature that differentiate the details.
Ranked #15 on
Fine-Grained Image Classification
on FGVC Aircraft
no code implementations • ECCV 2018 • Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, Nanning Zheng
In this paper, we propose Transductive Semi-Supervised Deep Learning (TSSDL) method that is effective for training Deep Convolutional Neural Network (DCNN) models.
no code implementations • 13 Apr 2018 • Shuai Zheng, Chris Ding, Feiping Nie
Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA).
no code implementations • 6 Apr 2018 • Shuai Zheng, Chris Ding
Support Vector Machine (SVM) is an efficient classification approach, which finds a hyperplane to separate data from different classes.
no code implementations • NeurIPS 2017 • Bo Jiang, Jin Tang, Chris Ding, Yihong Gong, Bin Luo
As a fundamental problem in computer vision, graph matching problem can usually be formulated as a Quadratic Programming (QP) problem with doubly stochastic and discrete (integer) constraints.
no code implementations • CVPR 2017 • Bo Jiang, Jin Tang, Chris Ding, Bin Luo
There are three main contributions of the proposed method: (1) we propose a new graph matching relaxation model, called Binary Constraint Preserving Graph Matching (BPGM), which aims to incorporate the discrete binary mapping constraints more in graph matching relaxation.
no code implementations • 20 Jun 2017 • Bo Jiang, Chris Ding
One interesting property of VOR is that how far an outlier lies away from its theoretically predicted value does not affect the final regularization and analysis results.
no code implementations • 28 May 2017 • Chris Ding, Bo Jiang
(1) A key property of outlier regularization is that how far an outlier lies away from its theoretically predicted value does not affect the final regularization and analysis results.
no code implementations • 23 May 2017 • Bo Jiang, Chris Ding, Bin Luo
One approach to deal with noise image data is to use data recovery techniques which aim to recover the true uncorrupted signals from the observed noise images.
no code implementations • 14 Oct 2016 • Shuai Zheng, Xiao Cai, Chris Ding, Feiping Nie, Heng Huang
Real life data often includes information from different channels.
no code implementations • 14 Oct 2016 • Shuai Zheng, Feiping Nie, Chris Ding, Heng Huang
In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix.
no code implementations • 14 Oct 2016 • Shuai Zheng, Chris Ding
The problem is to find a subspace to maximize the alignment between subspace-transformed data kernel and class indicator kernel.
no code implementations • 4 May 2016 • Shuai Zheng, Abhinav Vishnu, Chris Ding
Deep Learning is a very powerful machine learning model.
no code implementations • NeurIPS 2014 • Deguang Kong, Ryohei Fujimaki, Ji Liu, Feiping Nie, Chris Ding
Group lasso is widely used to enforce the structural sparsity, which achieves the sparsity at inter-group level.
no code implementations • CVPR 2013 • Hua Wang, Feiping Nie, Heng Huang, Chris Ding
We applied our SMML method to five broadly used object categorization and scene understanding image data sets for both singlelabel and multi-label image classification tasks.
no code implementations • CVPR 2013 • Bo Jiang, Chris Ding, Bio Luo, Jin Tang
Principal Component Analysis (PCA) is a widely used to learn a low-dimensional representation.
1 code implementation • SDM 2012 • Da Kuang, Chris Ding, Haesun Park
Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).