Results 141 to 150 of about 139,450 (186)

Inferential planning in the frontal cortex

Donnarumma F, Parr T, Friston K, Whittington J, Pezzulo G.   +4 more
europepmc   +1 more source

BeamCraft: Deep Reinforcement Learning-DrivenMulti-Objective Beamforming for ISAC

Dao DN, Miao Y.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Fused clustering mean estimation of central subspace

Journal of the Korean Statistical Society, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Um, Hye Yeon, Yoo, Jae Keun
openaire   +2 more sources

Central Subspace Dimensionality Reduction Using Covariance Operators

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
We 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.
Minyoung, Kim, Vladimir, Pavlovic
openaire   +2 more sources

Estimating central subspaces via inverse third moments

Biometrika, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yin, Xiangrong, Cook, R. Dennis
openaire   +1 more source

Exploring central subspace via contour regression

Journal of the Korean Statistical Society, 2013
Abstract Contour regression, a method for estimating the central subspace in regression, is based on estimating contour directions of small variation in the response. These directions span the orthogonal complement of the central subspace and can be extracted according to two measures of variation in the response: simple and general contour ...
Hakbae Lee, Pilkeun Choi
openaire   +1 more source

A central limit theorem for subspace algorithms

1997 European Control Conference (ECC), 1997
In 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.
Bauer, Dietmar, Deistler, Manfred, Scherrer, Wolfgang   +2 more
openaire   +2 more sources

Central Mean Subspace in Time Series

Journal of Computational and Graphical Statistics, 2009
We propose a notion of central mean dimension reduction subspace for time series {xt} which does not require specification of a model but seeks to find a p×d matrix Φd, d≤p, so that the d×1 vector ΦdTXt−1, where Xt−1=(xt−1, …, xt−p)T for some p≥1, includes all the information about xt that is available from E(xt|Xt−1).
Jin-Hong Park, T. N. Sriram, Xiangrong Yin   +2 more
openaire   +1 more source

Centralized joint sparse representation for multi-view subspace clustering

Journal of Intelligent & Fuzzy Systems, 2020
Multi-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 ...
Xie, Mengying, Liu, Xiaolan, Pan, Gan
openaire   +1 more source

Subspace Optimization in Centralized Noncoherent MIMO Radar

IEEE Transactions on Aerospace and Electronic Systems, 2011
We 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, Yih-Fang Huang, Zhenhua Gong, Mike Lemmon   +3 more
openaire   +1 more source