1 code implementation • 25 Feb 2024 • Baiang Li, Zhao Zhang, Huan Zheng, Xiaogang Xu, Yanyan Wei, Jingyi Zhang, Jicong Fan, Meng Wang
Our RTB is used for attention selection of rain-affected and unaffected regions and local modeling of mixed scales.
no code implementations • 25 Jan 2024 • Zixiao Wang, Dong Qiao, Jicong Fan
Discrete distribution clustering (D2C) was often solved by Wasserstein barycenter methods.
1 code implementation • 37th Conference on Neural Information Processing Systems (NeurIPS 2023) 2023 • Fangchen Yu, Runze Zhao, Zhan Shi, Yiwen Lu, Jicong Fan, Yicheng Zeng, Jianfeng Mao, Wenye Li
Secondly, we develop a series of affinity learning methods that equip the selfexpressive framework with ℓp-norm to construct an intrinsic affinity matrix with an adaptive extension.
no code implementations • 10 Oct 2023 • Jinyu Cai, Yunhe Zhang, Jicong Fan
Under the framework, we provide three algorithms with different computational efficiencies and stabilities for anomalous graph detection.
no code implementations • 18 Jul 2023 • Zhenhao Jiang, Biao Zeng, Hao Feng, Jin Liu, Jicong Fan, Jie Zhang, Jia Jia, Ning Hu, Xingyu Chen, Xuguang Lan
We propose a novel Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue.
no code implementations • 9 Jul 2023 • Feng Xiao, Ruoyu Sun, Jicong Fan
The core idea is to learn a mapping to transform the unknown distribution of training (normal) data to a known target distribution.
1 code implementation • 13 Feb 2023 • Yunhe Zhang, Yan Sun, Jinyu Cai, Jicong Fan
Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is not sufficiently compact, especially when the data are in high-dimensional spaces.
no code implementations • 5 Feb 2023 • Jinyu Cai, Yi Han, Wenzhong Guo, Jicong Fan
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar.
no code implementations • 2 Nov 2022 • Huan Zheng, Zhao Zhang, Jicong Fan, Richang Hong, Yi Yang, Shuicheng Yan
Specifically, we present a decoupled interaction module (DIM) that aims for sufficient dual-view information interaction.
no code implementations • 25 Jul 2022 • Yan Sun, Yi Han, Jicong Fan
Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces to extract hidden and useful information or facilitate visual understanding and interpretation of the data.
no code implementations • 9 Jun 2022 • Jinyu Cai, Wenzhong Guo, Jicong Fan
This work presents an unsupervised deep discriminant analysis for clustering.
no code implementations • 6 Jun 2022 • Jinyu Cai, Jicong Fan
This paper presents a simple yet effective method for anomaly detection.
no code implementations • 30 Apr 2022 • Yangcheng Gao, Zhao Zhang, Richang Hong, Haijun Zhang, Jicong Fan, Shuicheng Yan
To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated.
1 code implementation • CVPR 2022 • Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, Zhao Zhang
The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios.
no code implementations • ICLR 2022 • Jicong Fan
This paper presents a framework of multi-mode deep matrix and tensor factorizations to explore and exploit the full nonlinearity of the data in matrices and tensors.
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.
1 code implementation • 23 Jul 2021 • Jicong Fan, Yiheng Tu, Zhao Zhang, Mingbo Zhao, Haijun Zhang
First, we propose to find the most reliable affinity matrix via grid search or Bayesian optimization among a set of candidates given by different AMC methods with different hyperparameters, where the reliability is quantified by the \textit{relative-eigen-gap} of graph Laplacian introduced in this paper.
1 code implementation • 8 Dec 2020 • Jicong Fan
This paper presents a method called k-Factorization Subspace Clustering (k-FSC) for large-scale subspace clustering.
no code implementations • 7 Dec 2020 • Jicong Fan, Lijun Ding, Chengrun Yang, Zhao Zhang, Madeleine Udell
The theorems show that a relatively sharper regularizer leads to a tighter error bound, which is consistent with our numerical results.
no code implementations • 29 Jun 2020 • Lijun Ding, Jicong Fan, Madeleine Udell
This paper proposes a new variant of Frank-Wolfe (FW), called $k$FW.
1 code implementation • 7 Jun 2020 • Chengrun Yang, Jicong Fan, Ziyang Wu, Madeleine Udell
Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components.
no code implementations • 4 May 2020 • Jicong Fan, Chengrun Yang, Madeleine Udell
RNLMF constructs a dictionary for the data space by factoring a kernelized feature space; a noisy matrix can then be decomposed as the sum of a sparse noise matrix and a clean data matrix that lies in a low dimensional nonlinear manifold.
no code implementations • 6 Mar 2020 • Dong Yang, Monica Mengqi Li, Hong Fu, Jicong Fan, Zhao Zhang, Howard Leung
Overall, our work unified graph embedding features to promotes systematic research on human action recognition.
no code implementations • CVPR 2019 • Jicong Fan, Madeleine Udell
Recent advances in matrix completion enable data imputation in full-rank matrices by exploiting low dimensional (nonlinear) latent structure.
no code implementations • 15 Dec 2019 • Jicong Fan, Yuqian Zhang, Madeleine Udell
This paper develops new methods to recover the missing entries of a high-rank or even full-rank matrix when the intrinsic dimension of the data is low compared to the ambient dimension.
no code implementations • NeurIPS 2019 • Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell
Compared to the max norm and the factored formulation of the nuclear norm, factor group-sparse regularizers are more efficient, accurate, and robust to the initial guess of rank.
no code implementations • 28 Feb 2018 • Jicong Fan, Tommy W. S. Chow
RKPCA can be applied to many problems such as noise removal and subspace clustering and is still the only unsupervised nonlinear method robust to sparse noises.