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Evaluating probabilities under high-dimensional latent variable models
2008
Neural Information Processing Systems
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We present a simple new Monte Carlo algorithm for evaluating probabilities of observations in complex latent variable models, such as Deep Belief Networks. ...
In expectation, the log probability of a test set will be underestimated, and this could form the basis of a probabilistic bound. ...
In principle, there are many methods that could be applied to evaluating the probability assigned to data by a latent variable model. ...
dblp:conf/nips/MurrayS08
fatcat:4572m2m46vfbxollkhrzznjvr4
Latent Feature Decompositions for Integrative Analysis of Multi-Platform Genomic Data
2014
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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Index Terms Latent feature; genomic data; high-dimensional; interactions; integrative models; Bayesian model averaging Recently, integrative multi-platform analyses have demonstrated improvements in prediction ...
prediction and variable selection. ...
Figure 5 depicts high-probability models fit during the BMA procedure for all six choices of latent feature decomposition. ...
doi:10.1109/tcbb.2014.2325035
pmid:26146492
pmcid:PMC4486317
fatcat:qjekg5bmpfentboah65j34nivi
Semi-supervised deep learning for high-dimensional uncertainty quantification
[article]
2020
arXiv
pre-print
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An autoencoder is first adopted for mapping the high-dimensional space into a low-dimensional latent space, which contains a distinguishable failure surface. ...
Conventional uncertainty quantification methods usually lacks the capability of dealing with high-dimensional problems due to the curse of dimensionality. ...
High Dimensional Reliability Analysis Reliability analysis methods aim at evaluating the probability that an engineering system successfully performs its functionality with the consideration of various ...
arXiv:2006.01010v1
fatcat:btc3fmx4vza3basm5wxkrmrzni
A COGNITIVE MODEL OF PERCEPTIONS OF CLASS, STATUS AND LEVEL OF LIVING AMONG JAPANESE MEN: EVIDENCE FROM LATENT STRUCTURE ANALYSIS
1982
Behaviormetrika
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Recent development of latent structure analysis due to Goodman (1974a, b) enabled us to build a cognitive model of perceptions of class, status and level of living. ...
To test our model, the SSM survey data collected in 1975 were used. The evidence obtained from the present analysis confirmed our propositions. ...
However, due to the joint effect of latent variable Y, the probabilities of these pairs are not identical for the two levels of variable Y. ...
doi:10.2333/bhmk.9.11_61
fatcat:qa4p7oaeizeo7ecyt6r3vhfaey
Second order hierarchical partial least squares regression-polynomial chaos expansion for global sensitivity and reliability analyses of high-dimensional models
[article]
2019
arXiv
pre-print
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dimensional model representation. ...
To tackle the curse of dimensionality and multicollinearity problems of polynomial chaos expansion for analyzing global sensitivity and reliability of models with high stochastic dimensions, this paper ...
However, the required number of model evaluations dramatically increases with the number of model inputs. This problem is called the curse of dimensionality. ...
arXiv:1901.11295v3
fatcat:2ysd4lsa2zgihnslgszavnuuha
Clustering high-dimensional mixed data to uncover sub-phenotypes: joint analysis of phenotypic and genotypic data
2017
Statistics in Medicine
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A novel latent variable model which elegantly accommodates high dimensional, mixed data is developed to cluster LIPGENE-SU.VI.MAX participants using a Bayesian finite mixture model. ...
The LIPGENE-SU.VI.MAX study, like many others, recorded high dimensional continuous phenotypic data and categorical genotypic data. ...
Acknowledgements The authors would like to acknowledge the members of the Working Group on Statistical Learning at University College Dublin and the members of the Working Group on Model-based Clustering ...
doi:10.1002/sim.7371
pmid:28664564
fatcat:tz5pmpxlvzeixjmwkxrsmycedq
Task modeling in imitation learning using latent variable models
2010
2010 10th IEEE-RAS International Conference on Humanoid Robots
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In this paper we present a probabilistic model capable of modeling several different types of input sources within the same model. ...
Our model is capable to infer the task using only partial observations. ...
For an observed data point y v i , we can evaluate the marginal likelihood p(y v i |z i , Z) of the latent location under the model. ...
doi:10.1109/ichr.2010.5686348
dblp:conf/humanoids/EkSHK10
fatcat:jbepoqie3fhzhg62ms57sw7s2u
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction
[article]
2020
arXiv
pre-print
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In contrast, since the latent variables of conventional AEs are equally important for data reconstruction, they cannot be simply discarded to further reduce the dimensionality after the AE model is trained ...
Our proposed stochastic bottleneck framework enables seamless rate adaptation with high reconstruction performance, without requiring predetermined latent dimensionality at training. ...
Under a model-based approach of nonlinear eigenspectrum assumptions, we evaluated several parametric distributions for TailDrop probability, e.g., Poisson, Laplacian, exponential, sigmoid, Lorentzian, ...
arXiv:2005.02870v1
fatcat:lqugsbmrkbexzg4b3xpnaowt2i
Latent Class Analysis for Marketing Scale Development
2011
International Journal of Market Research
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Specifically, applying appropriate latent class models allows to assess scale validity and reliability more soundly than the methods traditionally used. ...
variables. ...
treat nominal and ordinal variables in the case of dimensionality evaluation. ...
doi:10.2501/ijmr-53-2-209-230
fatcat:xwhyxn7vbvgrvlgow2aokmdhki
Understanding the (un)interpretability of natural image distributions using generative models
[article]
2019
arXiv
pre-print
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Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions ...
More recent generative models leverage the power of neural networks to implicitly learn and represent probability models over complex images. ...
GANs are not bijective, and map a low-dimensional latent space to a high-dimensional image space. ...
arXiv:1901.01499v2
fatcat:rw3du3aeqbapbbjwu2u2pjv6ty
Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data
2010
Expert systems with applications
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2010 pre-print arXiv:1006.1030v1 fatcat:pwyymburmrgv5keheaufreigma
Increasingly used in psychology-related applications, Rasch model (RM) provides an appealing framework for handling high-dimensional microarray data. ...
The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (obser- vations), makes the application of many prediction techniques (e.g. ...
By latent variable model we mean any statistical model that relates a set of observed variables to set of latent variables (De Boeck & Wilson, 2004) . ...
doi:10.1016/j.eswa.2009.12.074
fatcat:gtkne6nbhfaxxarygj7oyfcuuu
Supervised Spectral Latent Variable Models
2009
Journal of machine learning research
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Technically this reduces to learning a probabilistic, input conditional model, over latent (manifold) and output variables using an alternation scheme. ...
The resulting Supervised Spectral Latent Variable Model (SSLVM) combines the properties of probabilistic geometric manifold learning (accommodates geometric constraints corresponding to any spectral embedding ...
Acknowledgements This work was supported, in part, by the EC and the NSF, under awards MCEXT-025481 and IIS-0535140. ...
dblp:journals/jmlr/BoS09
fatcat:r6ifzjcsqzbwndffqmzopysb3u
Probabilistic Feature Extraction from Multivariate Time Series Using Spatio-Temporal Constraints
[chapter]
2011
Lecture Notes in Computer Science
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A novel nonlinear probabilistic feature extraction method, called Spatio-Temporal Gaussian Process Latent Variable Model, is introduced to discover generalised and continuous low dimensional representation ...
This is achieved by incorporating a new spatio-temporal constraining prior over latent spaces within the likelihood optimisation of Gaussian Process Latent Variable Models (GPLVM). ...
As a result, GPLVM produces a complete joint probability distribution over latent and observed variables. ...
doi:10.1007/978-3-642-20847-8_15
fatcat:ru35l7fm4vd2vc4hialscntkl4
Context and observation driven latent variable model for human pose estimation
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
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We extend the Gaussian process latent variable model (GPLVM) to include an embedding from observation space (the space of image features) to the latent space. ...
While generative approaches can accurately determine human pose from image observations, they are computationally expensive due to search in the high dimensional human pose space. ...
While either searching or learning a prior model in a high dimensional space is expensive, dimensionality reduction techniques can be used to embed the high-dimensional pose space in a lower dimensional ...
doi:10.1109/cvpr.2008.4587511
dblp:conf/cvpr/GuptaCCKD08
fatcat:no7czbsnxjgmbeju7qbyyxyo4y
A Multidimensional Unfolding Latent Trait Model for Binary Data
2005
Social Science Research Network
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As a result, cell probabilities can be computed also in closed form, regardless of the dimensionality of the latent traits. ...
We introduce a multidimensional latent trait model for binary data with non-monotone item response functions. ...
Thus, the joint moments of the MVB distribution under this model are obtained by evaluating the k-dimensional normal density (5). ...
doi:10.2139/ssrn.1016119
fatcat:72kuildv55fffnspjc2bg67qt4
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