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What are the cases in which Isomap fails to do a good job?


What is dimensionality reduction? What is the difference between feature selection and extraction?High-dimensional data: What are useful techniques to know?Dimension Reduction - After or Before Train-Test SplitIssue with Spark SVDWhat are 2D dimensionality reduction algorithms good for?Dimensionality reduction with PCA limitationsWhat should we do with the construction of a classifier (e.g., NN) if we have more number of input features?Which dissimilarity/similarity measure use after a dimension reduction ( PCA / AutoEncoder / … )?Applying dimensionality reduction on OneHotEncoded arrayEfficient dimensionality reduction for large dataset













3












$begingroup$


As above, what is a possible scenario/ dataset/ case in which Isomap fails to do a decent dimensionality reduction?










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    3












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    As above, what is a possible scenario/ dataset/ case in which Isomap fails to do a decent dimensionality reduction?










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      3












      3








      3


      1



      $begingroup$


      As above, what is a possible scenario/ dataset/ case in which Isomap fails to do a decent dimensionality reduction?










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      As above, what is a possible scenario/ dataset/ case in which Isomap fails to do a decent dimensionality reduction?







      machine-learning scikit-learn dimensionality-reduction






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      asked Mar 18 at 4:44









      John DeteriousJohn Deterious

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          $begingroup$

          Here is the visualization of COIL-20 data set in t-SNE paper:



          t-SNE vs isomap on COIL-20 data set



          The data-set consists of images of 20 objects (clusters). In all cases provided in the paper and some cases I found on the Internet (6000 MNIST data set, slide no. 31), the quality of IsoMap (2001) is not comparable to t-SNE (2008).






          share|improve this answer









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            1












            $begingroup$

            Here is the visualization of COIL-20 data set in t-SNE paper:



            t-SNE vs isomap on COIL-20 data set



            The data-set consists of images of 20 objects (clusters). In all cases provided in the paper and some cases I found on the Internet (6000 MNIST data set, slide no. 31), the quality of IsoMap (2001) is not comparable to t-SNE (2008).






            share|improve this answer









            $endgroup$

















              1












              $begingroup$

              Here is the visualization of COIL-20 data set in t-SNE paper:



              t-SNE vs isomap on COIL-20 data set



              The data-set consists of images of 20 objects (clusters). In all cases provided in the paper and some cases I found on the Internet (6000 MNIST data set, slide no. 31), the quality of IsoMap (2001) is not comparable to t-SNE (2008).






              share|improve this answer









              $endgroup$















                1












                1








                1





                $begingroup$

                Here is the visualization of COIL-20 data set in t-SNE paper:



                t-SNE vs isomap on COIL-20 data set



                The data-set consists of images of 20 objects (clusters). In all cases provided in the paper and some cases I found on the Internet (6000 MNIST data set, slide no. 31), the quality of IsoMap (2001) is not comparable to t-SNE (2008).






                share|improve this answer









                $endgroup$



                Here is the visualization of COIL-20 data set in t-SNE paper:



                t-SNE vs isomap on COIL-20 data set



                The data-set consists of images of 20 objects (clusters). In all cases provided in the paper and some cases I found on the Internet (6000 MNIST data set, slide no. 31), the quality of IsoMap (2001) is not comparable to t-SNE (2008).







                share|improve this answer












                share|improve this answer



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                answered Mar 18 at 11:35









                EsmailianEsmailian

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