Results 261 to 270 of about 141,001 (306)

Dimensionality reduction simplifies synaptic partner matching in an olfactory circuit. [PDF]

Science
Lyu C, Li Z, Xu C, Wong KKL, Luginbuhl DJ, McLaughlin CN, Xie Q, Li T, Li H, Luo L.   +9 more
europepmc   +1 more source

Integrated Hyperparameter Optimization with Dimensionality Reduction and Clustering for Radiomics: A Bootstrapped Approach. [PDF]

Multimodal Technol Interact
Pawan SJ, Muellner M, Lei X, Desai M, Varghese B, Duddalwar V, Cen SY.   +6 more
europepmc   +1 more source

Inflammation Unchecked: Concurrent Kawasaki Disease and Stevens‐Johnson Syndrome in an 18‐Month‐Old Child

Arthritis Care &Research, EarlyView.
Catherine Deffendall, Jodi Backalar, Elizabeth Acevedo Zapata, Adriana Della Porta, Lorin Bibb, Tyler Reese, Charlotte M. Brown   +6 more
wiley   +1 more source

Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data

Woollard M, Panwar P, Harland LT, Mo J, Zhang H, Swarbrick A, Gottgens B, Ghazanfar S, Nghiem LH.   +8 more
europepmc   +1 more source

Dimensionality reduction [PDF]

, 2022
Dimensionality reduction is an Exploratory Data Analysis (EDA) approach allowing for fast visualization of high-dimensional data and the possibility of discovering hidden systematic patterns within a data set.
Hugo Jair Escalante, Eduardo F. Morales
openaire   +2 more sources

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Dimensionality reduction and generalization

Proceedings of the 24th international conference on Machine learning, 2007
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning problems. We show that performing KPCA and then ordinary least squares on the projected data, a procedure known as kernel principal component regression (KPCR), is equivalent ...
MOSCI, SOFIA, ROSASCO, LORENZO, VERRI, ALESSANDRO   +2 more
openaire   +2 more sources

Local dimensionality reduction

Computational Statistics, 1999
Different methods of dimensionality reduction such as principal components and Fisher's linear discriminant (FLD) are considered. The authors are interested in local versions of these methods based on normal mixtures and nearest neighbors approach. The Iterated Nearest Neighbor FLD (INN) is an example of such methods. Suppose, that a training sample of
David J. Marchette, Wendy L. Poston
openaire   +2 more sources

Dimensionality Reduction for Classification

2008
We investigate the effects of dimensionality reduction using different techniques and different dimensions on six two-class data sets with numerical attributes as pre-processing for two classification algorithms. Besides reducing the dimensionality with the use of principal components and linear discriminants, we also introduce four new techniques ...
Frank Plastria, Steven De Bruyne, Emilio Carrizosa   +2 more
openaire   +1 more source

Robust linear dimensionality reduction

IEEE Transactions on Visualization and Computer Graphics, 2004
We present a novel family of data-driven linear transformations, aimed at finding low-dimensional embeddings of multivariate data, in a way that optimally preserves the structure of the data. The well-studied PCA and Fisher's LDA are shown to be special members in this family of transformations, and we demonstrate how to generalize these two methods ...
Yehuda Koren, Liran Carmel
openaire   +2 more sources