Kernel principal component analysis

Applied sciences
Information engineering
Data science
Dimensionality reduction

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Related publications (29)

Principal Component Analysis By Optimization Of Symmetric Functions Has No Spurious Local Optima

Armin Eftekhari

Principal component analysis (PCA) finds the best linear representation of data and is an indispensable tool in many learning and inference tasks. Classically, principal components of a dataset are interpreted as the directions that preserve most of its "e ...

SIAM PUBLICATIONS2020

MOSES: A Streaming Algorithm for Linear Dimensionality Reduction

Armin Eftekhari

This paper introduces Memory-limited Online Subspace Estimation Scheme (MOSES) for both estimating the principal components of streaming data and reducing its dimension. More specifically, in various applications such as sensor networks, the data vectors a ...

IEEE COMPUTER SOC2020

Oblivious Sketching of High-Degree Polynomial Kernels

Mikhail Kapralov, Amir Zandieh

Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimensional spaces. A major disadvantage of kernel methods is their poor scalabili ...

ASSOC COMPUTING MACHINERY2020

Structure-property maps with Kernel principal covariates regression

Michele Ceriotti, Guillaume André Jean Fraux, Benjamin Aaron Helfrecht

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PC ...

2020

A multimodal approach to capture post-stroke temporal dynamics of recovery

Dimitri Nestor Alice Van De Ville, Silvestro Micera, Jenifer Cléa Miehlbradt, Nawal Noëlle Kinany, Elvira Pirondini, Martina Coscia, Christian Giang, Camilla Pierella

Objective.Several training programs have been developed in the past to restore motor functions after stroke. Their efficacy strongly relies on the possibility to assess individual levels of impairment and recovery rate. However, commonly used clinical scal ...

2020

A new kind of atlas of zeolite building blocks

Michele Ceriotti, Benjamin Aaron Helfrecht, Rocio Semino Barbaresi, Giovanni Pireddu

We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented by classical descriptors, such as distances, angles, and ring sizes, or whether ...

2019

Dynamic mode decomposition of resting-state and task fMRI

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois

Component analysis is a powerful tool to identify dominant patterns of interactions in multivariate datasets. In the context of fMRI data, methods such as principal component analysis or independent component analysis have been used to identify the brain n ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2019

Time-Frequency Characterization of Resting-State Brain Function Reveals Overlapping Components with Specific Topology and Frequency Content

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton

Even at rest, functional magnetic resonance imaging (fMRI) data displays exquisitely complex temporal dynamics. Here, we deployed a time-frequency analysis to track the modulus of fMRI signals over time, across space (a set of 341 brain areas) and frequenc ...

ASSOC COMPUTING MACHINERY2019

A visual working memory dataset collection with bootstrap Independent Component Analysis for comparison of electroencephalographic preprocessing pipelines

Fiorenzo Artoni

Here we present an electroencephalographic (EEG) collection of 71-channel datasets recorded from 14 subjects (7 males, 7 females, aged 20–40 years) while performing a visual working memory task with a T set of 150 Independent Component Analysis (ICA) decom ...

2019

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