Topic
Latent variable model
About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.
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TL;DR: This paper improves the map smoothing by introducing multiple regularization terms, one associated with each of the basis functions, to increase the stiffness of the mapping and avoid data over-fitting.
Abstract: Generative topographic mapping is a nonlinear latent variable model introduced by Bishop et al. as a probabilistic reformulation of self-organizing maps. The complexity of this model is mostly determined by the number and form of basis functions generating the nonlinear mapping from latent space to data space, but it can be further controlled by adding a regularization term to increase the stiffness of the mapping and avoid data over-fitting. In this paper, we improve the map smoothing by introducing multiple regularization terms, one associated with each of the basis functions. A similar technique to that of automatic relevance determination, our selective map smoothing locally controls the stiffness of the mapping depending on length scales of the underlying manifold, while optimizing the effective number of active basis functions.
37 citations
TL;DR: The authors proposed a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions, namely latent confounders, by testing independence between estimated external influences and finding subsets (parcels) that include variables unaffected by latent confounding.
Abstract: We consider learning a causal ordering of variables in a linear nongaussian acyclic model called LiNGAM Several methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct But the estimation results could be distorted if some assumptions are violated In this letter, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders The key idea is to detect latent confounders by testing independence between estimated external influences and find subsets (parcels) that include variables unaffected by latent confounders We demonstrate the effectiveness of our method using artificial data and simulated brain imaging data
37 citations
21 Oct 2017
TL;DR: It is shown how the combination of a statistical semantic model and a visual model can improve on the task of mapping images to their associated scene description, and achieves superior performance compared to the state-of-the-art method from the Stanford computer vision group.
Abstract: Structured scene descriptions of images are useful for the automatic processing and querying of large image databases. We show how the combination of a statistical semantic model and a visual model can improve on the task of mapping images to their associated scene description. In this paper we consider scene descriptions which are represented as a set of triples (subject, predicate, object), where each triple consists of a pair of visual objects, which appear in the image, and the relationship between them (e.g. man-riding-elephant, man-wearing-hat). We combine a standard visual model for object detection, based on convolutional neural networks, with a latent variable model for link prediction. We apply multiple state-of-the-art link prediction methods and compare their capability for visual relationship detection. One of the main advantages of link prediction methods is that they can also generalize to triples which have never been observed in the training data. Our experimental results on the recently published Stanford Visual Relationship dataset, a challenging real world dataset, show that the integration of a statistical semantic model using link prediction methods can significantly improve visual relationship detection. Our combined approach achieves superior performance compared to the state-of-the-art method from the Stanford computer vision group.
37 citations
TL;DR: It is shown that a linear acyclic model for latent factors is identifiable when the data are non-Gaussian.
37 citations
27 Jun 2015
TL;DR: This paper proposes a probabilistic matrix factorization approach with implicit correlation regularization to solve the problem of co-invocation of services in mashups, and develops a latent variable model to uncover the latent connections between services by analyzing their co-Invocation patterns.
Abstract: In this paper, we explore service recommendation and selection in the reusable composition context. The goal is to aid developers finding the most appropriate services in their composition tasks. We specifically focus on mashups, a domain that increasingly targets people without sophisticated programming knowledge. We propose a probabilistic matrix factorization approach with implicit correlation regularization to solve this problem. In particular, we advocate that the co-invocation of services in mashups is driven by both explicit textual similarity and implicit correlation of services, and therefore develop a latent variable model to uncover the latent connections between services by analyzing their co-invocation patterns. We crawled a real dataset from Programmable Web, and extensively evaluated the effectiveness of our proposed approach.
36 citations