Open AccessBook Chapter10.1002/9780470090183.CH5

Principal Component Analysis

- 11 May 2007

- pp 145-187

About: The article was published on 11 May 2007. and is currently open access. The article focuses on the topics: Principal component analysis.

AI Agents for this Paper

Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps

Most frequently asked questions

1. What contributions have the authors mentioned in the paper "Principal component analysis" ?

Principal component analysis ( pca ) this paper is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables.

Figure 4: Circle of correlations and plot of the loadings of (a) the variables with principal components 1 and 2, and (b) the variables and supplementary variables with principal components 1 and 2. Note that the supplementary variables are not positioned on the unit circle.

Figure 9: pca example: Amount of Francs spent (per month) on food type by social class and number of children. Correlations (and circle of correlations) of the variables with components 1 and 2. λ1 = 3,023,141.24, τ1 = 88%; λ2 = 290,575.84, τ2 = 8%.

Table 16: ca punctuation. Factor scores, contributions, mass, mass × squared factor scores, inertia to barycenter, and squared cosines for the rows. The positive important contributions are highlighted in light pink , and the negative

Figure 10: ca punctuation. The projections of the rows and the columns are displayed in the same map. λ1 = .0178, τ1 = 76.16; λ2 = .0056, τ2 = 23.84

Figure 2: Plot of the centered data, with the first and second components. The projections (or coordinates) of the word “neither” on the first and the second components are equal to −5.60 and −2.38.

Table 15: The punctuation marks of six French writers (from Brunet, 1989). The column labelled xi+ gives the total number of punctuation marks used by each author. N is the grand total of the data table. The vector of mass for the rows, r, is the proportion of punctuation marks used by each author (ri = xi+/N). The row labelled x+j gives the total number of times each punctuation mark was used. The centroid row, cT, gives the proportion of each punctuation mark in the sample (cj = x+j/N).

Citations

Journal Article•10.1016/J.NEUCOM.2016.07.005

Large-scale distance metric learning for k-nearest neighbors regression

Bac Nguyen, +2 more

- 19 Nov 2016

- Neurocomputing

TL;DR: This paper defines the constraints based on triplets, which are built from the neighborhood of each training instance, to learn the distance metric, and proposes a novel dual coordinate descent method for this type of problem.

...read moreread less

•Journal Article•10.1186/S12880-017-0179-7

Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification.

Muhammad Laiq Ur Rahman Shahid, +5 more

- 14 Feb 2017

- BMC Medical Imaging

TL;DR: A context-based automatic segmentation algorithm to delineate the fat pads from magnetic resonance images in a population-based study and has potential to be applied for the study of large data to understand the pathogenesis of the OSA syndrome.

...read moreread less

Journal Article•10.1016/j.chemosphere.2022.137263

Nano-TiO2 modifies heavy metal bioaccumulation in Daphnia magna: A model study.

Ying Wang, +5 more

- 01 Nov 2022

- Chemosphere

TL;DR: In this article , quantitative structure bioaccumulation relationship (QSBAR) models were established to explore the relationships between physicochemical parameters of heavy metals and their accumulation in Daphnia magna in the absence and presence of nano-TiO2 at low metal exposure concentrations.

...read moreread less

Proceedings Article•10.1109/ICDMW.2019.00080

Random Features and Random Neurons for Brain-Inspired Big Data Analytics

Mandar Gogate, +2 more

- 01 Nov 2019

TL;DR: This work applies random projection to generate a multi-dimensional vector space of commonsense knowledge and uses an extreme learning machine to perform reasoning on it to allow for both better representation of high-dimensional data and more efficient discovery of their semantic and affective relatedness.

...read moreread less

Book Chapter•10.1007/978-1-4939-3106-4_5

Outlier Detection for Mass Spectrometric Data

HyungJun Cho, +1 more

- 01 Jan 2016

- Methods of Molecular Biology

TL;DR: In this article, quantile regression methods for identifying outliers with low replication are presented, while a software program is introduced to demonstrate how to apply these procedures in the R environment system.

...read moreread less

...

Expand

Principal Component Analysis

Chat with Paper

AI Agents for this Paper

Most frequently asked questions

1. What contributions have the authors mentioned in the paper "Principal component analysis" ?

Figures

Citations

Large-scale distance metric learning for k-nearest neighbors regression

Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification.

Nano-TiO2 modifies heavy metal bioaccumulation in Daphnia magna: A model study.

Random Features and Random Neurons for Brain-Inspired Big Data Analytics

Outlier Detection for Mass Spectrometric Data

Related Papers (5)

Principal Component Analysis

Principal component analysis versus fuzzy principal component analysis

Principal components analysis

Principal component analysis of three-way data

Nonlinear Principal Component Analysis