Explore: Principal Component Analysis

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Source: The Open Library

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1Multivariate Statistical Methods

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“Multivariate Statistical Methods” Metadata:

  • Title: ➤  Multivariate Statistical Methods
  • Authors:
  • Language: English
  • Number of Pages: Median: 452
  • Publisher: ➤  Dowden, Hutchins & Ross - distributed by Halsted Press - John Wiley & Sons Inc - Dowden, Hutchinson & Ross, Inc.
  • Publish Date:
  • Publish Location: ➤  Stroudsburg, Pa - [New York] - Stroudsburg, Pennsylvania, USA

“Multivariate Statistical Methods” Subjects and Themes:

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Access and General Info:

  • First Year Published: 1975
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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2Constrained Principal Component Analysis and Related Techniques

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“Constrained Principal Component Analysis and Related Techniques” Metadata:

  • Title: ➤  Constrained Principal Component Analysis and Related Techniques
  • Author:
  • Language: English
  • Number of Pages: Median: 248
  • Publisher: ➤  Taylor & Francis Group - CRC, Taylor & Francis Group
  • Publish Date:
  • Publish Location: Boca Raton

“Constrained Principal Component Analysis and Related Techniques” Subjects and Themes:

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Access and General Info:

  • First Year Published: 2013
  • Is Full Text Available: No
  • Is The Book Public: No
  • Access Status: No_ebook

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32-D page map analysis

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“2-D page map analysis” Metadata:

  • Title: 2-D page map analysis
  • Authors:
  • Language: English
  • Number of Pages: Median: 331
  • Publisher: Springer New York
  • Publish Date:

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Access and General Info:

  • First Year Published: 2016
  • Is Full Text Available: No
  • Is The Book Public: No
  • Access Status: No_ebook

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4Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems

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“Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems” Metadata:

  • Title: ➤  Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems
  • Author:
  • Language: English
  • Publisher: ➤  Institution of Engineering & Technology
  • Publish Date:

“Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems” Subjects and Themes:

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Access and General Info:

  • First Year Published: 2021
  • Is Full Text Available: No
  • Is The Book Public: No
  • Access Status: No_ebook

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Principal component analysis

Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data

Kernel principal component analysis

multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel

Functional principal component analysis

Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data. Using this

Robust principal component analysis

Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of principal component analysis (PCA) which works

L1-norm principal component analysis

principal component analysis (L1-PCA) is a general method for multivariate data analysis. L1-PCA is often preferred over standard L2-norm principal component

Principal component regression

In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). PCR is a form

Factor analysis

(2009). "Principal component analysis vs. exploratory factor analysis" (PDF). SUGI 30 Proceedings. Retrieved 5 April 2012. SAS Statistics. "Principal Components

Component analysis

Component analysis may refer to one of several topics in statistics: Principal component analysis, a technique that converts a set of observations of

Dimensionality reduction

fewer dimensions. The data transformation may be linear, as in principal component analysis (PCA), but many nonlinear dimensionality reduction techniques

Nonlinear dimensionality reduction

and principal component analysis. High dimensional data can be hard for machines to work with, requiring significant time and space for analysis. It also


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