Search for tag: "pca"

22 Media

05:31 Unsupervised Learning Unsupervised Learning	Unsupervised Learning Unsupervised Learning hds dataset learning cluster clusters features pca component algorithm instance components variance direction reduction assignment point dimensions words dimensionality centers points From Dimitrios Doudesis on June 8th, 2020 0 likes 77 plays 0 Course Code GLHE11086 Licence Type Creative Commons - Attribution Share A Like
02:17 Properties of eigenfaces Properties of eigenfaces	Properties of eigenfaces Properties of eigenfaces iaml pca faces face eigenspace distance head pixels lighting things function space sample examples image eigenfaces downside upside representation identification thine dies From Nigel Goddard on September 25th, 2016 1 likes 1,033 plays 0 Licence Type Creative Commons - Attribution
01:59 Pros and cons of dimensionality reduction Pros and cons of dimensionality reduction	Pros and cons of dimensionality reduction Pros and cons of dimensionality reduction iaml pca space classification bayes dimensionality dimensions lot reason harm classes goal sets algorithms class downside upside assumption independence classifier classifiers From Nigel Goddard on September 25th, 2016 1 likes 1,566 plays 0 Licence Type Creative Commons - Attribution
02:39 Principal component analysis Principal component analysis	Principal component analysis Principal component analysis iaml pca hyperplane points coordinates variance space 3d point pc1 vectors sheet component coordinate pc3 direction dimensions technique attributes level r. From Nigel Goddard on September 25th, 2016 1 likes 1,577 plays 0 Licence Type Creative Commons - Attribution
03:00 Linear discriminant analysis Linear discriminant analysis	Linear discriminant analysis Linear discriminant analysis iaml pca class projection lda means dimension classifier variance direction counterexamples variances difference accuracy classification reds greens problem linear threshold hand From Nigel Goddard on September 25th, 2016 1 likes 1,172 plays 0 Licence Type Creative Commons - Attribution
01:52 Classification with PCA features Classification with PCA features	Classification with PCA features Classification with PCA features iaml pca dimension points class variance classes classifier swamps slide space colors point projection alright distribution dimensions coordinates decomposition labels redundancy From Nigel Goddard on September 25th, 2016 1 likes 1,214 plays 0 Licence Type Creative Commons - Attribution
01:51 When principal components fail When principal components fail	When principal components fail When principal components fail iaml pca attribute linear thing variance covariance dimension projection component matrix sheet subspace subspaces mind variances deviation distances fact gazillion attributes From Nigel Goddard on September 25th, 2016 1 likes 1,143 plays 0 Licence Type Creative Commons - Attribution
03:56 Eigenface representation Eigenface representation	Eigenface representation Eigenface representation iaml pca face faces representation eigenfaces pixels bit eigenvector vector amount guy projection components average numbers coordinate result human phase attributes eigenvectors From Nigel Goddard on September 25th, 2016 1 likes 1,133 plays 0 Licence Type Creative Commons - Attribution
05:00 Eigen-faces Eigen-faces	Eigen-faces Eigen-faces iaml pca matrix vector face row faces guys variance areas eigenvector numbers thing eigenvectors vectors pixels hair regions sort bit picture values From Nigel Goddard on September 25th, 2016 1 likes 1,247 plays 0 Licence Type Creative Commons - Attribution
03:48 Principal component analysis for the impatient Principal component analysis for the impatient	Principal component analysis for the impatient Principal component analysis for the impatient iaml pca dimension eigenvectors dimensions eigenvalues points space language matrix covariance height vector level hand sense duke bit grader worship ref From Nigel Goddard on September 25th, 2016 1 likes 1,234 plays 0 Licence Type Creative Commons - Attribution
03:56 How many principal components to use How many principal components to use	How many principal components to use How many principal components to use iaml pca variance eigenvalues sum dimensions lambda eigenvectors point dimensionality graph space decrease elbow inflection plot means scri sentences numbers sense humans From Nigel Goddard on September 25th, 2016 1 likes 1,212 plays 0 Licence Type Creative Commons - Attribution
06:02 Eigenvalue = variance along eigenvector Eigenvalue = variance along eigenvector	Eigenvalue = variance along eigenvector Eigenvalue = variance along eigenvector iaml pca eigenvector sum covariance variance lambda matrix thing times row ea attribute jth instances eigen elements component attributes 's summations dot From Nigel Goddard on September 25th, 2016 1 likes 1,245 plays 0 Licence Type Creative Commons - Attribution
11:54 Eigenvector = direction of maximum variance Eigenvector = direction of maximum variance	Eigenvector = direction of maximum variance Eigenvector = direction of maximum variance iaml pca vector variance covariance lambda attribute sum dot times element attributes numbers e. row eigenvector matrix equations equation derivative length points From Nigel Goddard on September 25th, 2016 2 likes 1,602 plays 0 Licence Type Creative Commons - Attribution
02:13 Low-dimensional projections of data Low-dimensional projections of data	Low-dimensional projections of data Low-dimensional projections of data iaml pca From Nigel Goddard on September 25th, 2016 0 likes 1,442 plays 0 Licence Type Creative Commons - Attribution
05:20 Finding eigenvalues and eigenvectors Finding eigenvalues and eigenvectors	Finding eigenvalues and eigenvectors Finding eigenvalues and eigenvectors iaml pca eigenvector matrix component length vector eigenvalues dimensions diagonal covariance unit thing components lambda equation eigenvectors eigenvalue values times determinant trick From Nigel Goddard on September 25th, 2016 1 likes 1,490 plays 0 Licence Type Creative Commons - Attribution
08:27 Principal components = eigenvectors Principal components = eigenvectors	Principal components = eigenvectors Principal components = eigenvectors iaml pca covariance matrix vector attribute minus slope points point dimension eigenvectors direction variance means numbers instance vectors dataset eigenvalues component plot From Nigel Goddard on September 25th, 2016 1 likes 1,704 plays 0 Licence Type Creative Commons - Attribution

the university of edinburgh

University affiliations

The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336, VAT Registration Number GB 592 9507 00, and is acknowledged by the UK authorities as a “Recognised body” which has been granted degree awarding powers.

Unless explicitly stated otherwise, all material is copyright © The University of Edinburgh 2019. Course specific materials are created by University of Edinburgh staff for you to use in your studies. You should not share, publish or sell these outside the University