Constructing Robust Affinity Graphs for Spectral Clustering

Zhu, Xiatian; Loy, Chen Change; Gong, Shaogang

doi:10.1109/cvpr.2014.188

Cited by 97 publications

(60 citation statements)

References 16 publications

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“…Therefore, the recent trend in measuring visual similarity is to use "learning metrics" that establish a computational measure via ad hoc learning [1,4,30]. With learning metrics, the problem is that they depend on used training images and a selected objective function, and it is unclear how they generalise beyond images in the training set.…”

Section: A Pairwise Visual Similarity Measurementioning

confidence: 99%

“…In recent works, Aghazadeh et al [1] and Dong et al [4] establish their similarity measures using classification scores of the exemplar SVM [17] which forms own classifier for each sample. Another similarity measure was proposed [30] using feature's tree distances in unsupervised random clustering forests. Learning similarity measures can be time consuming since they may change if new images are added and therefore our measure will be based on pairwise structural similarity combining local part appearance and part configuration.…”

Section: Related Workmentioning

confidence: 99%

“…Visual networks -Methods employing a network (graph) structure to represent visual relationships have recently been proposed [4,10,19,22,29,30]. Most of these algorithms aim at finding classes or a specific object automatically [10,22,29].…”

Section: Related Workmentioning

confidence: 99%

“…In view of this, unsupervised approaches that can automatically discover latent attributes [1,4,10,30] are considered and adopted. The popular unsupervised tool is clustering, but it omits the fact that often latent attributes are multi-relational and thus breaking them to discrete "modes" is not sensible -what is anyway the more dominant attribute, car pose or model?…”

Section: Introductionmentioning

confidence: 99%

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Discovering Multi-relational Latent Attributes by Visual Similarity Networks

Yancheshmeh

Kämäräinen

Chen

2015

Computer Vision - ACCV 2014 Workshops

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Abstract. The key problems in visual object classification are: learning discriminative feature to distinguish between two or more visually similar categories ( e.g. dogs and cats), modeling the variation of visual appearance within instances of the same class (e.g. Dalmatian and Chihuahua in the same category of dogs), and tolerate imaging distortion (3D pose). These account to within and between class variance in machine learning terminology, but in recent works these additional pieces of information, latent dependency, have been shown to be beneficial for the learning process. Latent attribute space was recently proposed and verified to capture the latent dependent correlation between classes. Attributes can be annotated manually, but more attempting is to extract them in an unsupervised manner. Clustering is one of the popular unsupervised approaches, and the recent literature introduces similarity measures that help to discover visual attributes by clustering. However, the latent attribute structure in real life is multi-relational, e.g. two different sport cars in different poses vs. a sport car and a family car in the same posewhat attribute can dominate similarity? Instead of clustering, a network (graph) containing multiple connections is a natural way to represent such multi-relational attributes between images. In the light of this, we introduce an unsupervised framework for network construction based on pairwise visual similarities and experimentally demonstrate that the constructed network can be used to automatically discover multiple discrete (e.g. sub-classes) and continuous (pose change) latent attributes. Illustrative examples with publicly benchmarking datasets can verify the effectiveness of capturing multi-relation between images in the unsupervised style by our proposed network.

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Section: A Pairwise Visual Similarity Measurementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discovering Multi-relational Latent Attributes by Visual Similarity Networks

Yancheshmeh

Kämäräinen

Chen

2015

Computer Vision - ACCV 2014 Workshops

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“…In the field of text mining and image processing, there emerging many novel methods to integrate multi-view dataset [11][12][13][14][15]. Co-training spectral clustering algorithm [16] attempts to find compatible clustering solution by updating iteratively discriminative eigenvectors of each view.…”

Section: Introductionmentioning

confidence: 99%

A Robust Symmetric Nonnegative Matrix Factorization Framework for Clustering Multiple Heterogeneous Microbiome Data

Ma¹,

Hu²,

He³

et al. 2017

Preprint

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Integration of multi-view datasets which are comprised of heterogeneous sources or different representations is challenging to understand the subtle and complex relationship in data. Such data integration methods attempt to combine efficiently the complementary information of multiple data types to construct a comprehensive view of underlying data. Nonnegative matrix factorization (NMF), an approach that can be used for signal compression and noise reduction, has aroused widespread attention in the last two decades. The Kullback-Leibler divergence (or relative entropy) information distance can be used to measure the loss function of NMF. In this article, we propose a fast and robust framework (RSNMF) based on symmetric nonnegative matrix factorization (SNMF) and similarity network fusion (SNF) for clustering human microbiome data including functional, metabolic and phylogenetic profiles. Many existing methods typically utilize all the information provided by each view to create a consensus representation, which often suffers a lot from noise in data and cannot provide a precise representation of the latent data structures. In contrast, RSNMF combines the strength of SNMF and the advantage of SNF to form a robust clustering indicator matrix thus can reduce the noise influence. We conduct experiments on one synthetic and two real dataset (microbiome data, text data) and the results show that the proposed RSNMF has better performance over the baseline and the state-of-art methods, which demonstrates the potential application of RSNMF for microbiome data analysis.

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A general iterative clustering algorithm

Lin

Laska

Siegel

2022

Statistical Analysis

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The quality of a cluster analysis of unlabeled units depends on the quality of the between units dissimilarity measures. Data-dependent dissimilarity is more objective than data independent geometric measures such as Euclidean distance. As suggested by Breiman, many data driven approaches are based on decision tree ensembles, such as a random forest (RF), that produce a proximity matrix that can easily be transformed into a dissimilarity matrix. An RF can be obtained using labels that distinguish units with real data from units with synthetic data. The resulting dissimilarity matrix is input to a clustering program and units are assigned labels corresponding to cluster membership. We introduce a general iterative cluster (GIC) algorithm that improves the proximity matrix and clusters of the base RF. The cluster labels are used to grow a new RF yielding an updated proximity matrix, which is entered into the clustering program. The process is repeated until convergence. The same procedure can be used with many base procedures such as the extremely randomized tree ensemble. We evaluate the performance of the GIC algorithm using benchmark and simulated data sets. The properties measured by the Silhouette score are substantially superior to the base clustering algorithm. The GIC package has been released in R: https://cran.r-project.org/web/packages/GIC/index.html.

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Constructing Robust Affinity Graphs for Spectral Clustering

Cited by 97 publications

References 16 publications

Discovering Multi-relational Latent Attributes by Visual Similarity Networks

Discovering Multi-relational Latent Attributes by Visual Similarity Networks

A Robust Symmetric Nonnegative Matrix Factorization Framework for Clustering Multiple Heterogeneous Microbiome Data

A general iterative clustering algorithm

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