Large Graph Construction for Scalable Semi-Supervised Learning

By Wei David Liu, Junfeng He, Shih-Fu Chang. International Conference on Machine Learning, 2010

639 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/537ff50f531a40067a859391524523a0319514f0

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Citations in the corpus

Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “Another set of methods uses the relationship among data points and constructs a neighborhood graph [342], [343] or its variants [344].”
Discrete Graph Hashing (2014, methodology): “In this paper, we leverage the concept of Anchor Graphs [21] to capture the neighborhood structure inherent in a given massive dataset, and then formulate a graph-based hashing model over the whole dataset.”
Adaptive Hypergraph Learning and its Application in Image Classification (2012, unknown): “Among existing transductive learning methods, graph-based learning [5]–[8], [10], [11], [13], [14], [40], [44], [49] achieves promising performance.”
Large-scale Multi-view Subspace Clustering in Linear Time (2019, methodology): “Anchor graph is an efﬁcient way to construct the large-scale graph matrix (Chen and Cai 2011; Liu, He, and Chang 2010; Wang et al. 2016a; Han, Xiong, and Nie 2017).”
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition (2015, methodology): “Other more sophisticated methods may also be used, such as SC, soft assignment (SA) [24,25], orthogonal matching pursuit (OMP), locality-constrained linear coding [26], and local anchor embedding [27].”
Deep Spectral Clustering Using Dual Autoencoder Network (2019, methodology): “Next, a spectral clustering method is used to embed the latent representations into the eigenspace of their associated graph Laplacian matrix [25].”
Efficient kNN classification algorithm for big data (2016, background): “Thus, we need to find the matrix W which is projection matrix of X at the landmark matrix Z [10].”
Dimensionality reduction on Anchorgraph with an efficient Locality Preserving Projection (2016, unknown): “[12] suggested computing the adjacency matrix as”
Efficient Computation of k-Nearest Neighbour Graphs for Large High-Dimensional Data Sets on GPU Clusters (2013, background): “K-Nearest neighbor graphs have a variety of applications in bioinformatics [1,2], data mining [3], machine learning [4,5], manifold learning [6], clustering analysis [7], and pattern recognition [8].”
Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization (2016, background): “Anchor graph regularization (AGR) [21], [22], a recently proposed graph-based learning model for large datasets, constructs a novel graph with datapoints and anchors.”
Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition (2017, methodology): “Graph based approaches [39, 29] are used to link the unlabeled image instances to labeled ones (which are served as anchors) and propagate labels by the graph topology and connectiveness weights.”
Segmentation using superpixels: A bipartite graph partitioning approach (2012, background): “), then the induced affinity matrix WY over Y , WY := B⊤D−1 X B, is exactly the one-step transition probability matrix on the graph GY = {Y,B⊤D−1 X B}, or the two-step transition probability matrix on the original bipartite graph G = {X,Y,B} following Y → X → Y [17].”
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings (2020, methodology): “To address the scalability issue, inspired by previous anchor-based methods [39, 52], we design an anchor-based scalable metric learning technique which learns a node-anchor affinity matrix R P Rnˆs (i.”
Large-Scale Multi-View Spectral Clustering via Bipartite Graph (2015, methodology): “Our method is inspired by the large-scale semi-supervised learning algorithm proposed in (Liu, He, and Chang 2010).”
Label Propagation via Teaching-to-Learn and Learning-to-Teach (2017, background): “Recently, some researchers adapted the traditional label propagation methods to large-scale scenarios via either efﬁcient graph construction [11] or efﬁcient labeling [12].”
Learning to Hash for Indexing Big Data—A Survey (2015, methodology): “The key idea is to use a small set of MðM NÞ anchor points to approximate the graph structure represented by the matrix A such that the similarity between any pair of points can be approximated using point-to-anchor similarities [97].”