Principal component analysis | Wikipedia audio article
Channel: wikipedia tts
Date: 2018-12-27

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Principal_component_analysis


00:04:51 1 Intuition
00:06:21 2 Details
00:09:07 2.1 First component
00:12:44 2.2 Further components
00:12:56 2.3 Covariances
00:16:33 2.4 Dimensionality reduction
00:20:15 2.5 Singular value decomposition
00:20:29 3 Further considerations
00:24:56 4 Table of symbols and abbreviations
00:29:35 5 Properties and limitations of PCA
00:34:36 5.1 Properties
00:34:46 5.2 Limitations
00:34:57 5.3 PCA and information theory
00:41:41 6 Computing PCA using the covariance method
00:42:43 6.1 Organize the data set
00:45:59 6.2 Calculate the empirical mean
00:46:47 6.3 Calculate the deviations from the mean
00:47:30 6.4 Find the covariance matrix
00:48:16 6.5 Find the eigenvectors and eigenvalues of the covariance matrix
00:49:36 6.6 Rearrange the eigenvectors and eigenvalues
00:50:35 6.7 Compute the cumulative energy content for each eigenvector
00:52:53 6.8 Select a subset of the eigenvectors as basis vectors
00:53:16 6.9 Project the z-scores of the data onto the new basis
00:54:19 7 Derivation of PCA using the covariance method
00:55:54 8 Covariance-free computation
00:56:40 8.1 Iterative computation
00:58:45 8.2 The NIPALS method
00:59:27 8.3 Online/sequential estimation
01:03:25 9 PCA and qualitative variables
01:05:26 10 Applications
01:05:54 10.1 Quantitative finance
01:07:12 10.2 Neuroscience
01:07:21 11 Relation with other methods
01:08:31 11.1 Correspondence analysis
01:10:57 11.2 Factor analysis
01:11:07 11.3 iK/i-means clustering
01:12:06 11.4 Non-negative matrix factorization
01:13:58 12 Generalizations
01:14:37 12.1 Sparse PCA
01:16:37 12.2 Nonlinear PCA
01:16:47 12.3 Robust PCA
01:17:54 13 Similar techniques
01:19:37 13.1 Independent component analysis
01:20:46 13.2 Network component analysis
01:20:55 14 Software/source code
01:21:17 15 See also
01:23:05 16 References



Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago.

Learning by listening is a great way to:
- increases imagination and understanding
- improves your listening skills
- improves your own spoken accent
- learn while on the move
- reduce eye strain

Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone.

Listen on Google Assistant through Extra Audio:
https://assistant.google.com/services/invoke/uid/0000001a130b3f91
Other Wikipedia audio articles at:
https://www.youtube.com/results?search_query=wikipedia+tts
Upload your own Wikipedia articles through:
https://github.com/nodef/wikipedia-tts
Speaking Rate: 0.9965622235318787
Voice name: en-AU-Wavenet-A


"I cannot teach anybody anything, I can only make them think."
- Socrates


SUMMARY
=======
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. If there are 
  
    
      
        n
      
    
    {\displaystyle n}
   observations with 
  
    
      
        p
      
    
    {\displaystyle p}
   variables, then the number of distinct principal components is 
  
    
      
        min
        (
        n
        −
        1
        ,
        p
        )
      
    
    {\displaystyle \min(n-1,p)}
  . This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors (each being a linear combination of the variables and containing n observations) are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables.
PCA was invented in 1901 by Karl Pearson, as an analogue of the principal axis theorem in mechanics; it was later independently developed and named by Harold Hotelling in the 1930s. Depending on the field of application, it is also named the discrete Karhunen–Loève transform (KLT) in signal processing, the Hotelling transform in multivariate quality control, proper orthogonal decomposition (POD) in mechanical engineering, singular value decomposition (SVD) of X (Golub and Van Loan, 1983), eigenvalue decomposition (EVD) of XTX in linear algebra, factor analysis (for a discussion of the differences between PCA and factor analysis see Ch. 7 of Jolliffe's Principal Component Analysis), Eckart ...