Search Results for author: Jiyuan Liu

Found 13 papers, 5 papers with code

Contrastive Continual Multi-view Clustering with Filtered Structural Fusion

no code implementations26 Sep 2023 Xinhang Wan, Jiyuan Liu, Hao Yu, Ao Li, Xinwang Liu, Ke Liang, Zhibin Dong, En Zhu

Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning.

Clustering Contrastive Learning +1

Scalable Incomplete Multi-View Clustering with Structure Alignment

1 code implementation31 Aug 2023 Yi Wen, Siwei Wang, Ke Liang, Weixuan Liang, Xinhang Wan, Xinwang Liu, Suyuan Liu, Jiyuan Liu, En Zhu

Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID).

Clustering graph construction +2

One-step Multi-view Clustering with Diverse Representation

no code implementations8 Jun 2023 Xinhang Wan, Jiyuan Liu, Xinwang Liu, Siwei Wang, Yi Wen, Tianjiao Wan, Li Shen, En Zhu

In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework.

Clustering MULTI-VIEW LEARNING +1

Fast Continual Multi-View Clustering with Incomplete Views

no code implementations4 Jun 2023 Xinhang Wan, Bin Xiao, Xinwang Liu, Jiyuan Liu, Weixuan Liang, En Zhu

Such an incomplete continual data problem (ICDP) in MVC is tough to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views.

Clustering

Adversarial Attack with Raindrops

no code implementations28 Feb 2023 Jiyuan Liu, Bingyi Lu, Mingkang Xiong, Tao Zhang, Huilin Xiong

Extensive experiments are carried out to demonstrate that the images crafted by AdvRD are visually and statistically close to the natural raindrop images, can work as strong attackers to DNN models, and also help improve the robustness of DNNs to raindrop attacks.

Adversarial Attack Generative Adversarial Network

Auto-weighted Multi-view Clustering for Large-scale Data

1 code implementation21 Jan 2023 Xinhang Wan, Xinwang Liu, Jiyuan Liu, Siwei Wang, Yi Wen, Weixuan Liang, En Zhu, Zhe Liu, Lu Zhou

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views.

Clustering

Multiple Kernel Clustering with Dual Noise Minimization

no code implementations13 Jul 2022 Junpu Zhang, Liang Li, Siwei Wang, Jiyuan Liu, Yue Liu, Xinwang Liu, En Zhu

As a representative, late fusion MKC first decomposes the kernels into orthogonal partition matrices, then learns a consensus one from them, achieving promising performance recently.

Clustering

Multi-view Deep One-class Classification: A Systematic Exploration

no code implementations27 Apr 2021 Siqi Wang, Jiyuan Liu, Guang Yu, Xinwang Liu, Sihang Zhou, En Zhu, Yuexiang Yang, Jianping Yin

Third, to remedy the problem that limited benchmark datasets are available for multi-view deep OCC, we extensively collect existing public data and process them into more than 30 new multi-view benchmark datasets via multiple means, so as to provide a publicly available evaluation platform for multi-view deep OCC.

Classification General Classification +1

Localized Simple Multiple Kernel K-Means

1 code implementation ICCV 2021 Xinwang Liu, Sihang Zhou, Li Liu, Chang Tang, Siwei Wang, Jiyuan Liu, Yi Zhang

After that, we theoretically show that the objective of SimpleMKKM is a special case of this local kernel alignment criterion with normalizing each base kernel matrix.

Clustering

One-Pass Multi-View Clustering for Large-Scale Data

no code implementations ICCV 2021 Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Li Liu, Siqi Wang, Weixuan Liang, Jiangyong Shi

In this way, the generated partition can guide multi-view matrix factorization to produce more purposive coefficient matrix which, as a feedback, improves the quality of partition.

Clustering

SimpleMKKM: Simple Multiple Kernel K-means

1 code implementation11 May 2020 Xinwang Liu, En Zhu, Jiyuan Liu, Timothy Hospedales, Yang Wang, Meng Wang

We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM).

Clustering

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