Author name cluster
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
EAAI Journal 2026 Journal Article
IJCAI Conference 2023 Conference Paper
Traditional semi-supervised learning tasks assume that both labeled and unlabeled data follow the same class distribution, but the realistic open-world scenarios are of more complexity with unknown novel classes mixed in the unlabeled set. Therefore, it is of great challenge to not only recognize samples from known classes but also discover the unknown number of novel classes within the unlabeled data. In this paper, we introduce a new open-world semi-supervised novel class discovery approach named OpenNCD, a progressive bi-level contrastive learning method over multiple prototypes. The proposed method is composed of two reciprocally enhanced parts. First, a bi-level contrastive learning method is introduced, which maintains the pair-wise similarity of the prototypes and the prototype group levels for better representation learning. Then, a reliable prototype similarity metric is proposed based on the common representing instances. Prototypes with high similarities will be grouped progressively for known class recognition and novel class discovery. Extensive experiments on three image datasets are conducted and the results show the effectiveness of the proposed method in open-world scenarios, especially with scarce known classes and labels.
AAAI Conference 2020 Conference Paper
A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various largescale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.
IJCAI Conference 2020 Conference Paper
Online semi-supervised multi-label classification serves a practical yet challenging task since only a small number of labeled instances are available in real streaming environments. However, the mainstream of existing online classification techniques are focused on the single-label case, while only a few multi-label stream classification algorithms exist, and they are mainly trained on labeled instances. In this paper, we present a novel Online Semi-supervised Multi-Label learning algorithm (OnSeML) based on label compression and local smooth regression, which allows real-time multi-label predictions in a semi-supervised setting and is robust to evolving label distributions. Specifically, to capture the high-order label relationship and to build a compact target space for regression, OnSeML compresses the label set into a low-dimensional space by a fixed orthogonal label encoder. Then a locally defined regression function for each incoming instance is obtained with a closed-form solution. Targeting the evolving label distribution problem, we propose an adaptive decoding scheme to adequately integrate newly arriving labeled data. Extensive experiments provide empirical evidence for the effectiveness of our approach.
IJCAI Conference 2019 Conference Paper
Community detection and link prediction are highly dependent since knowing cluster structure as a priori will help identify missing links, and in return, clustering on networks with supplemented missing links will improve community detection performance. In this paper, we propose a Cluster-driven Low-rank Matrix Completion (CLMC), for performing community detection and link prediction simultaneously in a unified framework. To this end, CLMC decomposes the adjacent matrix of a target network as three additive matrices: clustering matrix, noise matrix and supplement matrix. The community-structure and low-rank constraints are imposed on the clustering matrix, such that the noisy edges between communities are removed and the resulting matrix is an ideal block-diagonal matrix. Missing edges are further learned via low-rank matrix completion. Extensive experiments show that CLMC achieves state-of-the-art performance.
YNICL Journal 2019 Journal Article
IJCAI Conference 2019 Conference Paper
Deep autoencoder is widely used in dimensionality reduction because of the expressive power of the neural network. Therefore, it is naturally suitable for embedding tasks, which essentially compresses high-dimensional information into a low-dimensional latent space. In terms of network representation, methods based on autoencoder such as SDNE and DNGR have achieved comparable results with the state-of-arts. However, all of them do not leverage label information, which leads to the embeddings lack the characteristic of discrimination. In this paper, we present Triplet Enhanced AutoEncoder (TEA), a new deep network embedding approach from the perspective of metric learning. Equipped with the triplet-loss constraint, the proposed approach not only allows capturing the topological structure but also preserving the discriminative information. Moreover, unlike existing discriminative embedding techniques, TEA is independent of any specific classifier, we call it the model-free property. Extensive empirical results on three public datasets (i. e, Cora, Citeseer and BlogCatalog) show that TEA is stable and achieves state-of-the-art performance compared with both supervised and unsupervised network embedding approaches on various percentages of labeled data. The source code can be obtained from https: //github. com/yybeta/TEA.