Dataset of extremely low-dimensional images for PCA Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsWhy does applying PCA on targets causes underfitting?Why training error is larger than validation error after PCA?Sales Dataset to determine best model for predicting future salesCould data from a test set 'leak' into predictor during PCA?AdaBoost implementation and tuning for high dimensional feature space in RConvolutional network for classification, extremely sensitive to lightingAre there any very good APIs for matching similar images?How should I retrain my neural network with new images?t-SNE on extremely high-dimensional spacesNeed inputs for Food data set to predict consumer ordering behaviour

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Dataset of extremely low-dimensional images for PCA



Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhy does applying PCA on targets causes underfitting?Why training error is larger than validation error after PCA?Sales Dataset to determine best model for predicting future salesCould data from a test set 'leak' into predictor during PCA?AdaBoost implementation and tuning for high dimensional feature space in RConvolutional network for classification, extremely sensitive to lightingAre there any very good APIs for matching similar images?How should I retrain my neural network with new images?t-SNE on extremely high-dimensional spacesNeed inputs for Food data set to predict consumer ordering behaviour










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I am looking for a public data-set of images that differ from each other only slightly, so that after applying PCA they can be reconstructed with a small error from very few PCA coefficients. It can be any type of images, the purpose is only to demonstrate an extreme example of PCA.










share|improve this question











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    1












    $begingroup$


    I am looking for a public data-set of images that differ from each other only slightly, so that after applying PCA they can be reconstructed with a small error from very few PCA coefficients. It can be any type of images, the purpose is only to demonstrate an extreme example of PCA.










    share|improve this question











    $endgroup$




    bumped to the homepage by Community 23 mins ago


    This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

















      1












      1








      1





      $begingroup$


      I am looking for a public data-set of images that differ from each other only slightly, so that after applying PCA they can be reconstructed with a small error from very few PCA coefficients. It can be any type of images, the purpose is only to demonstrate an extreme example of PCA.










      share|improve this question











      $endgroup$




      I am looking for a public data-set of images that differ from each other only slightly, so that after applying PCA they can be reconstructed with a small error from very few PCA coefficients. It can be any type of images, the purpose is only to demonstrate an extreme example of PCA.







      machine-learning dataset pca






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Jan 23 '18 at 12:27









      Vaalizaadeh

      7,61562265




      7,61562265










      asked Jan 23 '18 at 9:56









      elliotpelliotp

      111




      111





      bumped to the homepage by Community 23 mins ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







      bumped to the homepage by Community 23 mins ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.






















          1 Answer
          1






          active

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          0












          $begingroup$

          Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.






          share|improve this answer











          $endgroup$












          • $begingroup$
            Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
            $endgroup$
            – elliotp
            Jan 23 '18 at 12:16










          • $begingroup$
            @elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:26











          • $begingroup$
            @elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:48











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          0












          $begingroup$

          Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.






          share|improve this answer











          $endgroup$












          • $begingroup$
            Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
            $endgroup$
            – elliotp
            Jan 23 '18 at 12:16










          • $begingroup$
            @elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:26











          • $begingroup$
            @elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:48















          0












          $begingroup$

          Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.






          share|improve this answer











          $endgroup$












          • $begingroup$
            Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
            $endgroup$
            – elliotp
            Jan 23 '18 at 12:16










          • $begingroup$
            @elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:26











          • $begingroup$
            @elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:48













          0












          0








          0





          $begingroup$

          Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.






          share|improve this answer











          $endgroup$



          Actually in your case I guess the pure images are not that important. The features that you extract from them are important because if your feature space is constructed base on intensity of images at different picture elements, pixels, then you will need so many coefficients. As an easy solution, use MNIST digits and use shape features to extract features from the images of numbers. You can use plausible number of features and then use PCA for the data that is in the new feature space that you have just constructed. In this case smaller number of coefficients will be needed if the features are fine.







          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Jan 23 '18 at 11:22

























          answered Jan 23 '18 at 11:17









          VaalizaadehVaalizaadeh

          7,61562265




          7,61562265











          • $begingroup$
            Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
            $endgroup$
            – elliotp
            Jan 23 '18 at 12:16










          • $begingroup$
            @elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:26











          • $begingroup$
            @elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:48
















          • $begingroup$
            Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
            $endgroup$
            – elliotp
            Jan 23 '18 at 12:16










          • $begingroup$
            @elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:26











          • $begingroup$
            @elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
            $endgroup$
            – Vaalizaadeh
            Jan 23 '18 at 12:48















          $begingroup$
          Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
          $endgroup$
          – elliotp
          Jan 23 '18 at 12:16




          $begingroup$
          Thanks but as I said, I want to demonstrate PCA for the actual pixels of images.
          $endgroup$
          – elliotp
          Jan 23 '18 at 12:16












          $begingroup$
          @elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
          $endgroup$
          – Vaalizaadeh
          Jan 23 '18 at 12:26





          $begingroup$
          @elliotp You can use mnist to do so as well but I'm not sure how many coefficients will suffice for you purpose. I guess MNIST suites for your task because most of the numbers are centered, consequently center pixels with each other and marginal pixels with each other will have high correlation which may cause first principal components have great eigenvalues.
          $endgroup$
          – Vaalizaadeh
          Jan 23 '18 at 12:26













          $begingroup$
          @elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
          $endgroup$
          – Vaalizaadeh
          Jan 23 '18 at 12:48




          $begingroup$
          @elliotp also as a suggestion, I recommend you to pick sample of images of two different labels and plot the three eigenvalues with the greatest amounts.
          $endgroup$
          – Vaalizaadeh
          Jan 23 '18 at 12:48

















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          ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result