The Experts below are selected from a list of 199575 Experts worldwide ranked by ideXlab platform
Masashi Sugiyama - One of the best experts on this subject based on the ideXlab platform.
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policy search with high dimensional context variables
arXiv: Machine Learning, 2016Co-Authors: Voot Tangkaratt, Herke Van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi SugiyamaAbstract:Direct contextual policy search methods learn to improve policy parameters and simultaneously generalize these parameters to different context or task variables. However, learning from high-dimensional context variables, such as camera images, is still a prominent problem in many real-world tasks. A naive application of unsupervised Dimensionality reduction methods to the context variables, such as principal component analysis, is insufficient as task-relevant input may be ignored. In this paper, we propose a contextual policy search method in the model-based relative entropy stochastic search framework with integrated Dimensionality reduction. We learn a model of the reward that is locally quadratic in both the policy parameters and the context variables. Furthermore, we perform supervised linear Dimensionality reduction on the context variables by nuclear norm regularization. The experimental results show that the proposed method outperforms naive Dimensionality reduction via principal component analysis and a state-of-the-art contextual policy search method.
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Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis
Journal of Machine Learning Research, 2007Co-Authors: Masashi SugiyamaAbstract:Reducing the Dimensionality of data without losing intrinsic information is an important preprocessing step in high-dimensional data analysis. Fisher discriminant analysis (FDA) is a traditional technique for supervised Dimensionality reduction, but it tends to give undesired results if samples in a class are multimodal. An unsupervised Dimensionality reduction method called locality-preserving projection (LPP) can work well with multimodal data due to its locality preserving property. However, since LPP does not take the label information into account, it is not necessarily useful in supervised learning scenarios. In this paper, we propose a new linear supervised Dimensionality reduction method called local Fisher discriminant analysis (LFDA), which effectively combines the ideas of FDA and LPP. LFDA has an analytic form of the embedding transformation and the solution can be easily computed just by solving a generalized eigenvalue problem. We demonstrate the practical usefulness and high scalability of the LFDA method in data visualization and classification tasks through extensive simulation studies. We also show that LFDA can be extended to non-linear Dimensionality reduction scenarios by applying the kernel trick.
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local fisher discriminant analysis for supervised Dimensionality reduction
International Conference on Machine Learning, 2006Co-Authors: Masashi SugiyamaAbstract:Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper, we consider the supervised Dimensionality reduction problem where samples are accompanied with class labels. Traditional Fisher discriminant analysis is a popular and powerful method for this purpose. However, it tends to give undesired results if samples in some class form several separate clusters, i.e., multimodal. In this paper, we propose a new Dimensionality reduction method called local Fisher discriminant analysis (LFDA), which is a localized variant of Fisher discriminant analysis. LFDA takes local structure of the data into account so the multimodal data can be embedded appropriately. We also show that LFDA can be extended to non-linear Dimensionality reduction scenarios by the kernel trick.
Christopher Yau - One of the best experts on this subject based on the ideXlab platform.
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zifa Dimensionality reduction for zero inflated single cell gene expression analysis
Genome Biology, 2015Co-Authors: Emma Pierson, Christopher YauAbstract:Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical Dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a Dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.
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Dimensionality reduction for zero inflated single cell gene expression analysis
bioRxiv, 2015Co-Authors: Christopher Yau, Emma PiersonAbstract:Single cell RNA-seq data allows insight into normal cellular function and diseases including cancer through the molecular characterisation of cellular state at the single-cell level. Dimensionality reduction of such high-dimensional datasets is essential for visualization and analysis, but single-cell RNA-seq data is challenging for classical Dimensionality reduction methods because of the prevalence of dropout events leading to zero-inflated data. Here we develop a Dimensionality reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves performance on simulated and biological datasets.
Sangjin Sin - One of the best experts on this subject based on the ideXlab platform.
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shear viscosity instability and the upper bound of the gauss bonnet coupling constant
Journal of High Energy Physics, 2009Co-Authors: Sangjin SinAbstract:We compute the Dimensionality dependence of �/s for charged black branes with Gauss-Bonnet correction. We find that both causality and stability constrain the value of Gauss-Bonnet coupling constant to be bounded by 1/4 in the infinite Dimensionality limit. We further show that higher Dimensionality stabilize the gravitational perturbation. The stabilization of the perturbation in higher dimensional space-time is a straightforward consequence of the Gauss-Bonnet coupling constant bound.
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shear viscosity instability and the upper bound of the gauss bonnet coupling constant
arXiv: High Energy Physics - Theory, 2009Co-Authors: Sangjin SinAbstract:We compute the Dimensionality dependence of $\eta/s$ for charged black branes with Gauss-Bonnet correction. We find that both causality and stability constrain the value of Gauss-Bonnet coupling constant to be bounded by 1/4 in the infinite Dimensionality limit. We further show that higher Dimensionality stabilize the gravitational perturbation. The stabilization of the perturbation in higher dimensional space-time is a straightforward consequence of the Gauss-Bonnet coupling constant bound.
Marcio Das Chagas Moura - One of the best experts on this subject based on the ideXlab platform.
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deep variational auto encoders a promising tool for Dimensionality reduction and ball bearing elements fault diagnosis
Structural Health Monitoring-an International Journal, 2019Co-Authors: Gabriel San Martin, Enrique Lopez Droguett, Viviane Meruane, Marcio Das Chagas MouraAbstract:One of the main challenges that the industry faces when dealing with massive data for failure diagnosis is high Dimensionality of such data. This can be tackled by Dimensionality reduction method s...
Enrique Lopez Droguett - One of the best experts on this subject based on the ideXlab platform.
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deep variational auto encoders a promising tool for Dimensionality reduction and ball bearing elements fault diagnosis
Structural Health Monitoring-an International Journal, 2019Co-Authors: Gabriel San Martin, Enrique Lopez Droguett, Viviane Meruane, Marcio Das Chagas MouraAbstract:One of the main challenges that the industry faces when dealing with massive data for failure diagnosis is high Dimensionality of such data. This can be tackled by Dimensionality reduction method s...
