Results 261 to 270 of about 139,695 (308)

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

Real-Time EEG Decoding of Motor Imagery via Nonlinear Dimensionality Reduction (Manifold Learning) and Shallow Classifiers. [PDF]

Biosensors (Basel)
Kucukselbes H, Sayilgan E.
europepmc   +1 more source

Seeking Precise Protein-like Functions from Random Heteropolymer Ensemble and through Dimensionality Reduction. [PDF]

ACS Cent Sci
Wu G, Jin T, Zhou H, Coley CW, Alexander-Katz A, Lu H.   +5 more
europepmc   +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

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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
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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
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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