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10:23 ML3: Dimensionality Reduction using Principal… ML3: Dimensionality Reduction using Principal Component Analysis	ML3: Dimensionality Reduction using Principal Component Analysis In this video, we will learn that we can use Principal Component Analysis (PCA) to find a transformation matrix that minimises reconstruction error for dimensionality reduction. We also show how this… matrix reconstruction dimensions jackson george error dimension components space x. columns variation transformation points dataset spectra clips audio speakers pca From Elliot Crowley on November 5th, 2020 0 likes 4 plays 0
09:00 MLP Lecture 06 - Clip 03 - Data Normalisation MLP Lecture 06 - Clip 03 - Data Normalisation	MLP Lecture 06 - Clip 03 - Data Normalisation Machine Learning Practical (MLP) Lecture 06, Clip 03 / 05. machine learning deep learning training network components pca correlation thing solution directions sets inputs samples idea whites eigenvalues space analysis test access deviation optimization From Pavlos Andreadis on October 24th, 2020 1 likes 201 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
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 1,411 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 2,001 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 2,068 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 1,540 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 1,616 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 1,530 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 1,591 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 1,770 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 1,654 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 1,628 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 1,669 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 2,082 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 1,939 plays 0 Licence Type Creative Commons - Attribution

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