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Feature filter for estimating central mean subspace and its sparse solution
Computational Statistics and Data Analysis, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiangrong Yin, Richard Kryscio
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A Shrinkage Estimation of Central Subspace in Sufficient Dimension Reduction
Communications in Statistics Part B: Simulation and Computation, 2010Sliced regression is an effective dimension reduction method by replacing the original high-dimensional predictors with its appropriate low-dimensional projection. It is free from any probabilistic assumption and can exhaustively estimate the central subspace.
Qin Wang
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A central limit theorem for subspace algorithms
1997 European Control Conference (ECC), 1997In the last few years, the so called ‘subspace-algorithms’ have become a quite popular tool for the estimation of linear dynamic systems. However their statistical properties are not fully clarified right now. Earlier papers investigated the consistency of the method. This paper presents a central limit theorem for the estimates.
Dietmar Bauer, W Scherrer
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Fused clustering mean estimation of central subspace
Journal of the Korean Statistical Society, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jae Keun Yoo
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Estimation and inference on central mean subspace for multivariate response data
Computational Statistics and Data Analysis, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liping Zhu
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Analysing nonlinear time series with central subspace
Journal of Statistical Computation and Simulation, 2012Traditionally, time series analysis involves building an appropriate model and using either parametric or nonparametric methods to make inference about the model parameters. Motivated by recent developments for dimension reduction in time series, an empirical application of sufficient dimension reduction (SDR) to nonlinear time series modelling is ...
Jin-Hong Park
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Central Subspace Dimensionality Reduction Using Covariance Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011We consider the task of dimensionality reduction informed by real-valued multivariate labels. The problem is often treated as Dimensionality Reduction for Regression (DRR), whose goal is to find a low-dimensional representation, the central subspace, of the input data that preserves the statistical correlation with the targets.
Vladimir Pavlovic, Minyoung Kim
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Subspace Optimization in Centralized Noncoherent MIMO Radar
IEEE Transactions on Aerospace and Electronic Systems, 2011We consider the problem of subspace optimization for centralized noncoherent multiple input-multiple output (MIMO) radar based on various measures such as capacity, diversity, and probability of detection. In subspace centralized noncoherent MIMO radar (SC-MIMO), a subset of stations is selected based on channel knowledge or channel statistics to ...
Thomas G. Pratt +3 more
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Centralized joint sparse representation for multi-view subspace clustering
Journal of Intelligent & Fuzzy Systems, 2020Multi-view subspace clustering arises in many computer visional tasks such as object recognition and image segmentation. The basic idea is to measure the same instance with multiple views. In this paper, we proposed two centralized joint sparse representation models, namely, Centralized Global Joint Sparse Representation (CGJSR) and Centralized Local ...
Mengying Xie, Xiaolan Liu 0003, Gan Pan
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Partial central subspace and sliced average variance estimation
Journal of Statistical Planning and Inference, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sanford Weisberg, R Dennis Cook
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