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1,898
Leveraging relational autocorrelation with latent group models
- In MRDM '05: Proceedings of the 4th international workshop on Multi-relational mining. ACM
"... Abstract. The presence of autocorrelation provides strong motivation for using relational techniques for learning and inference. Autocorrelation is a statistical dependency between the values of the same variable on related entities and is a nearly ubiquitous characteristic of relational data sets. ..."
Abstract
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Cited by 80 (23 self)
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—the presence of underlying groups that influence the attributes on a set of entities. We propose a latent group model (LGM) for relational data, which discovers and exploits the hidden structures responsible for the observed autocorrelation among class labels. Modeling the latent group structure improves model
Latent groups and cross-level inferences
- Elect. Stud
, 2001
"... Abstract It is common for the only available data on interesting political phenomena to be aggregated at a level above the micro-unit in question. Analysis of voting behavior in elections for which survey data are unavailable is a case in point: one often must draw inferences about voters by analys ..."
Abstract
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Cited by 4 (0 self)
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small samples. These successes, however, translate only to situations in which data are available on the group of interest. Models for how to make cross-level inferences remain plagued by widespread disagreement. The ability to make consistently reliable inferences to individuals from aggregate data
Latent grouping models for user preference prediction
, 2009
"... We tackle the problem of new users or documents in collaborative filtering. Generalization over users by grouping them into user groups is beneficial when a rating is to be predicted for a relatively new document having only few observed ratings. Analogously, generalization over documents improves p ..."
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introduced a probabilistic latent grouping model for predicting the relevance of a document to a user. The model assumes a latent group structure for both users and items. We compare the model against a state-of-the-art method, the User Rating Profile model, where only the users have a latent group structure
ABSTRACT Leveraging Relational Autocorrelation with Latent Group Models
"... The presence of autocorrelation provides strong motivation for using relational techniques for learning and inference. Autocorrelation is a statistical dependency between the values of the same variable on related entities and is a nearly ubiquitous characteristic of relational data sets. Recent res ..."
Abstract
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of underlying groups that influence the attributes on a set of entities. We propose a latent group model (LGM) for relational data, which discovers and exploits the hidden structures responsible for the observed autocorrelation among class labels. Modeling the latent group structure improves model performance
Two-Way Latent Grouping Model for User Preference Prediction
- In Proceedings of the UAI'05
, 2005
"... We introduce a novel latent grouping model for predicting the relevance of a new document to a user. The model assumes a latent group structure for both users and documents. ..."
Abstract
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Cited by 10 (5 self)
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We introduce a novel latent grouping model for predicting the relevance of a new document to a user. The model assumes a latent group structure for both users and documents.
Proximal methods for the latent group lasso penalty
, 2014
"... We consider a regularized least squares problem, with regularization by structured sparsity-inducing norms, which extend the usual `1 and the group lasso penalty, by allowing the subsets to overlap. Such regularizations lead to nonsmooth problems that are difficult to optimize, and we propose in thi ..."
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We consider a regularized least squares problem, with regularization by structured sparsity-inducing norms, which extend the usual `1 and the group lasso penalty, by allowing the subsets to overlap. Such regularizations lead to nonsmooth problems that are difficult to optimize, and we propose
A Bayesian latent group analysis for detecting poor effort in the assessment of malingering
, 2012
"... Abstract Despite their theoretical appeal, Bayesian methods for the assessment of poor effort and malingering are still rarely used in neuropsychological research and clinical diagnosis. In this article, we outline a novel and easy-to-use Bayesian latent group analysis of malingering whose goal is ..."
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Cited by 2 (1 self)
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Abstract Despite their theoretical appeal, Bayesian methods for the assessment of poor effort and malingering are still rarely used in neuropsychological research and clinical diagnosis. In this article, we outline a novel and easy-to-use Bayesian latent group analysis of malingering whose goal
Learning Latent Groups with Hinge-loss Markov Random Fields
"... Probabilistic models with latent variables are powerful tools that can help explain related phenomena by mediating dependencies among them. Learning in the presence of latent variables can be difficult though, because of the difficulty of marginalizing them out, or, more commonly, maximizing a lower ..."
Abstract
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Cited by 5 (3 self)
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Probabilistic models with latent variables are powerful tools that can help explain related phenomena by mediating dependencies among them. Learning in the presence of latent variables can be difficult though, because of the difficulty of marginalizing them out, or, more commonly, maximizing a
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models
"... In large-scale applications of undirected graphical models, such as social networks and biological networks, similar patterns occur frequently and give rise to simi-lar parameters. In this situation, it is beneficial to group the parameters for more efficient learning. We show that even when the gro ..."
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Cited by 1 (0 self)
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In large-scale applications of undirected graphical models, such as social networks and biological networks, similar patterns occur frequently and give rise to simi-lar parameters. In this situation, it is beneficial to group the parameters for more efficient learning. We show that even when
Results 1 - 10
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1,898
