arXiv research

This research examines the geometry of latent spaces in push-forward generative models.

Online distributional prediction with latent cluster geometry

Deep generative models are tremendously successful in learning low-dimensional latent representations that well-describe the data. These representations, however, tend to much distort relationships between points, i.e. pairwise distances tend to not reflect semantic similarities well. This renders unsupervised tasks, s…

Proposes a geometry-aware VAE for better latent space modeling.

Affinity propagation is one of the most effective unsupervised pattern recognition algorithms for data clustering in high-dimensional feature space. However, the numerous attempts to test its performance for community detection in complex networks have been attaining results very far from the state of the art methods s…

New algorithm clusters hyperspectral images at multiple scales.

Generative Adversarial networks (GANs) have obtained remarkable success in many unsupervised learning tasks and unarguably, clustering is an important unsupervised learning problem. While one can potentially exploit the latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not re…

HypeGBMS clusters data in hyperbolic space, overcoming Euclidean limitations.

A new approach clusters data first, then embeds each cluster, improving transparency.

This paper explores how the latent structure affects clustering in GAN-generated data.

This paper addresses the problem of unsupervised clustering which remains one of the most fundamental challenges in machine learning and artificial intelligence. We propose the clustered generator model for clustering which contains both continuous and discrete latent variables. Discrete latent variables model the clus…

Enhances interpretability of linear latent spaces through automated clustering and ranking.

ESRLCM clusters similar responses, more broadly than traditional models.

The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods. In this paper, we propose a joint lea…

Simple Deep LDA models achieve accuracy competitive with softmax baselines.

Paper calculates the exact error of LDA models.

Optimal algorithm for latent bandits with cluster structure reduces regret to nearly optimal.

New spectral clustering method handles discrete covariates for better community detection.

Paper presents a reparameterized DP-DLGMM for clustering.

Spectral clustering with edge counting detects communities in sparse models.

Our work improves VAE latent space clustering by enforcing invariant and equivariant learning.

Dimensionality reduction techniques play an essential role in data analytics, signal processing and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations tha…

In clustering we normally output one cluster variable for each datapoint. However it is not necessarily the case that there is only one way to partition a given dataset into cluster components. For example, one could cluster objects by their colour, or by their type. Different attributes form a hierarchy, and we could …

Improves clustering performance by mixing latent representations.

Finite mixture models have become a popular tool for clustering. Amongst other uses, they have been applied for clustering longitudinal data and clustering high-dimensional data. In the latter case, a latent Gaussian mixture model is sometimes used. Although there has been much work on clustering using latent variables…

Motivated by modern applications in which one constructs graphical models based on a very large number of features, this paper introduces a new class of cluster-based graphical models, in which variable clustering is applied as an initial step for reducing the dimension of the feature space. We employ model assisted cl…

Identifying customer segments in retail banking portfolios with different risk profiles can improve the accuracy of credit scoring. The Variational Autoencoder (VAE) has shown promising results in different research domains, and it has been documented the powerful information embedded in the latent space of the VAE. We…

SEMASIA provides a large dataset of latent representations for model comparison.

Convex optimization method infers latent structure in random dot product graphs.

GCML preserves geometric structure in manifold clustering for diverse data types.

Study provides selective inference method for latent block models.

Much of the data being created on the web contains interactions between users and items. Stochastic blockmodels, and other methods for community detection and clustering of bipartite graphs, can infer latent user communities and latent item clusters from this interaction data. These methods, however, typically ignore t…

Proposes a neural network method to improve consistencies in high dimensional data analysis.

A novel MCMC method clusters data faster and more accurately.

New algorithms for latent class analysis using regularized spectral clustering.

Improves hierarchical clustering in Euclidean space using autoencoders.

Clustering of data sets is a standard problem in many areas of science and engineering. The method of spectral clustering is based on embedding the data set using a kernel function, and using the top eigenvectors of the normalized Laplacian to recover the connected components. We study the performance of spectral clust…

VampPrior Mixture Model improves clustering in DLVMs.

New computational lower bounds for clustering and related problems.

Unified framework for clustering with auxiliary data.

Improved co-clustering for robust data analysis.

In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by which the categorical ou…

New method improves clustering accuracy in noisy single-cell data.

A new model clusters networks with community-specific submanifold structures.

Perfect clustering achieved in hypergraphs with enough interactions.

Hierarchical learning models, such as mixture models and Bayesian networks, are widely employed for unsupervised learning tasks, such as clustering analysis. They consist of observable and hidden variables, which represent the given data and their hidden generation process, respectively. It has been pointed out that co…

In this paper, we seek a clinically-relevant latent code for representing the spectrum of macular disease. Towards this end, we construct retina-VAE, a variational autoencoder-based model that accepts a patient profile vector (pVec) as input. The pVec components include clinical exam findings and demographic informatio…

Variation Autoencoder (VAE) has become a powerful tool in modeling the non-linear generative process of data from a low-dimensional latent space. Recently, several studies have proposed to use VAE for unsupervised clustering by using mixture models to capture the multi-modal structure of latent representations. This st…