PCA in visual Analytics2019 Community Moderator ElectionPCA on Neural Networks dimensions reduction?How to Interpret the output of PCA?Dimensionality reduction with PCA limitationsCould data from a test set 'leak' into predictor during PCA?How to use pca results for linear regressionDistributed PCA or an equivalentDifferences between applying KMeans over PCA and applying PCA over KMeansShow importance of variables from a data set without a response variable? Use PCA?
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PCA in visual Analytics
2019 Community Moderator ElectionPCA on Neural Networks dimensions reduction?How to Interpret the output of PCA?Dimensionality reduction with PCA limitationsCould data from a test set 'leak' into predictor during PCA?How to use pca results for linear regressionDistributed PCA or an equivalentDifferences between applying KMeans over PCA and applying PCA over KMeansShow importance of variables from a data set without a response variable? Use PCA?
$begingroup$
I m studying visual analytics and i have a theoretical question about this topic.
My professor introduced this schema in him slide
For connect data to visualisation.
Some topic is very easy to understand and regards the rappresentation of data, plot and other tool, other topic instead regards the "data" in particular the data reduction using algorithm like PCA.
I m not very clear why using PCA over the data for reduction some data that we want rappresent.
Ad example, if i have dataset with all movie(it s just an example) with some attribute ad example
id,title,year,review, userReview ecc...
Where is the correlation of this data?Why i need apply a PCA for reduce big database and lose some of this components that I can visualize in some plot or schema that for me is important
For example could be interesting see all best movie that have high average review, using PCA we just cluster user in different movie (i saw example on Iris database here: https://medium.com/@kyasar.mail/pca-principal-component-analysis-729068e28ec8 )
Anyone can clarify me?
visualization data pca dimensionality-reduction
asked Feb 23 at 17:57
theantomctheantomc
143
$endgroup$
bumped to the homepage by Community♦ 1 hour ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I m studying visual analytics and i have a theoretical question about this topic.
My professor introduced this schema in him slide
For connect data to visualisation.
Some topic is very easy to understand and regards the rappresentation of data, plot and other tool, other topic instead regards the "data" in particular the data reduction using algorithm like PCA.
I m not very clear why using PCA over the data for reduction some data that we want rappresent.
Ad example, if i have dataset with all movie(it s just an example) with some attribute ad example
id,title,year,review, userReview ecc...
Where is the correlation of this data?Why i need apply a PCA for reduce big database and lose some of this components that I can visualize in some plot or schema that for me is important
For example could be interesting see all best movie that have high average review, using PCA we just cluster user in different movie (i saw example on Iris database here: https://medium.com/@kyasar.mail/pca-principal-component-analysis-729068e28ec8 )
Anyone can clarify me?
visualization data pca dimensionality-reduction
asked Feb 23 at 17:57
theantomctheantomc
143
$endgroup$
bumped to the homepage by Community♦ 1 hour ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I m studying visual analytics and i have a theoretical question about this topic.
My professor introduced this schema in him slide
For connect data to visualisation.
Some topic is very easy to understand and regards the rappresentation of data, plot and other tool, other topic instead regards the "data" in particular the data reduction using algorithm like PCA.
I m not very clear why using PCA over the data for reduction some data that we want rappresent.
Ad example, if i have dataset with all movie(it s just an example) with some attribute ad example
id,title,year,review, userReview ecc...
Where is the correlation of this data?Why i need apply a PCA for reduce big database and lose some of this components that I can visualize in some plot or schema that for me is important
For example could be interesting see all best movie that have high average review, using PCA we just cluster user in different movie (i saw example on Iris database here: https://medium.com/@kyasar.mail/pca-principal-component-analysis-729068e28ec8 )
Anyone can clarify me?
visualization data pca dimensionality-reduction
asked Feb 23 at 17:57
theantomctheantomc
143
$endgroup$
I m studying visual analytics and i have a theoretical question about this topic.
My professor introduced this schema in him slide
For connect data to visualisation.
Some topic is very easy to understand and regards the rappresentation of data, plot and other tool, other topic instead regards the "data" in particular the data reduction using algorithm like PCA.
I m not very clear why using PCA over the data for reduction some data that we want rappresent.
Ad example, if i have dataset with all movie(it s just an example) with some attribute ad example
id,title,year,review, userReview ecc...
Where is the correlation of this data?Why i need apply a PCA for reduce big database and lose some of this components that I can visualize in some plot or schema that for me is important
For example could be interesting see all best movie that have high average review, using PCA we just cluster user in different movie (i saw example on Iris database here: https://medium.com/@kyasar.mail/pca-principal-component-analysis-729068e28ec8 )
Anyone can clarify me?
visualization data pca dimensionality-reduction
visualization data pca dimensionality-reduction
asked Feb 23 at 17:57
theantomctheantomc
143
asked Feb 23 at 17:57
theantomctheantomc
143
asked Feb 23 at 17:57
theantomctheantomc
143
asked Feb 23 at 17:57
theantomctheantomc
143
asked Feb 23 at 17:57
theantomctheantomc
143
143
bumped to the homepage by Community♦ 1 hour 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♦ 1 hour ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
You have to see things for what they are. First, you have to figure out what PCA does. It's not clustering, but it can be used to realize that there could be clusters in the data.
PCA is not always the answer. It depends on the data. There are other dimensionality reduction techniques such as autoencoders, non-negative matrix factorization and so on...
For the movie example, it does not make sense to use PCA. The iris database contains another type of data, where it does make sense. For the movie example, you might want to have a look at singular value decomposition and collaborative filtering.
One of the reasons for doing PCA could be for clustering. If you have many dimensions, and the intrinsic dimensionality is low you will have the curse of dimensionality. Reducing the dimensionality by PCA and doing clustering on the embeddings solves, in this case, the curse of dimensionality.
answered Feb 23 at 20:19
Carl RynegardhCarl Rynegardh
30119
$endgroup$
$begingroup$
I already do SVD for big data analysis in other course and i had challange with this stuff , all reduction (PCA,SVD, MDS, TSNE , ecc) but in this course is mandatory take a dataset and use a PCA and data visual (in D3). The professor link reduction with visualizzation , but i dont understand how
$endgroup$
– theantomc
Feb 24 at 8:59
add a comment |
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1 Answer
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1 Answer
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$begingroup$
You have to see things for what they are. First, you have to figure out what PCA does. It's not clustering, but it can be used to realize that there could be clusters in the data.
PCA is not always the answer. It depends on the data. There are other dimensionality reduction techniques such as autoencoders, non-negative matrix factorization and so on...
For the movie example, it does not make sense to use PCA. The iris database contains another type of data, where it does make sense. For the movie example, you might want to have a look at singular value decomposition and collaborative filtering.
One of the reasons for doing PCA could be for clustering. If you have many dimensions, and the intrinsic dimensionality is low you will have the curse of dimensionality. Reducing the dimensionality by PCA and doing clustering on the embeddings solves, in this case, the curse of dimensionality.
answered Feb 23 at 20:19
Carl RynegardhCarl Rynegardh
30119
$endgroup$
$begingroup$
I already do SVD for big data analysis in other course and i had challange with this stuff , all reduction (PCA,SVD, MDS, TSNE , ecc) but in this course is mandatory take a dataset and use a PCA and data visual (in D3). The professor link reduction with visualizzation , but i dont understand how
$endgroup$
– theantomc
Feb 24 at 8:59
add a comment |
$begingroup$
You have to see things for what they are. First, you have to figure out what PCA does. It's not clustering, but it can be used to realize that there could be clusters in the data.
PCA is not always the answer. It depends on the data. There are other dimensionality reduction techniques such as autoencoders, non-negative matrix factorization and so on...
For the movie example, it does not make sense to use PCA. The iris database contains another type of data, where it does make sense. For the movie example, you might want to have a look at singular value decomposition and collaborative filtering.
One of the reasons for doing PCA could be for clustering. If you have many dimensions, and the intrinsic dimensionality is low you will have the curse of dimensionality. Reducing the dimensionality by PCA and doing clustering on the embeddings solves, in this case, the curse of dimensionality.
answered Feb 23 at 20:19
Carl RynegardhCarl Rynegardh
30119
$endgroup$
$begingroup$
I already do SVD for big data analysis in other course and i had challange with this stuff , all reduction (PCA,SVD, MDS, TSNE , ecc) but in this course is mandatory take a dataset and use a PCA and data visual (in D3). The professor link reduction with visualizzation , but i dont understand how
$endgroup$
– theantomc
Feb 24 at 8:59
add a comment |
$begingroup$
You have to see things for what they are. First, you have to figure out what PCA does. It's not clustering, but it can be used to realize that there could be clusters in the data.
PCA is not always the answer. It depends on the data. There are other dimensionality reduction techniques such as autoencoders, non-negative matrix factorization and so on...
For the movie example, it does not make sense to use PCA. The iris database contains another type of data, where it does make sense. For the movie example, you might want to have a look at singular value decomposition and collaborative filtering.
One of the reasons for doing PCA could be for clustering. If you have many dimensions, and the intrinsic dimensionality is low you will have the curse of dimensionality. Reducing the dimensionality by PCA and doing clustering on the embeddings solves, in this case, the curse of dimensionality.
answered Feb 23 at 20:19
Carl RynegardhCarl Rynegardh
30119
$endgroup$
You have to see things for what they are. First, you have to figure out what PCA does. It's not clustering, but it can be used to realize that there could be clusters in the data.
PCA is not always the answer. It depends on the data. There are other dimensionality reduction techniques such as autoencoders, non-negative matrix factorization and so on...
For the movie example, it does not make sense to use PCA. The iris database contains another type of data, where it does make sense. For the movie example, you might want to have a look at singular value decomposition and collaborative filtering.
One of the reasons for doing PCA could be for clustering. If you have many dimensions, and the intrinsic dimensionality is low you will have the curse of dimensionality. Reducing the dimensionality by PCA and doing clustering on the embeddings solves, in this case, the curse of dimensionality.
answered Feb 23 at 20:19
Carl RynegardhCarl Rynegardh
30119
edited Feb 23 at 20:26
answered Feb 23 at 20:19
Carl RynegardhCarl Rynegardh
30119
answered Feb 23 at 20:19
Carl RynegardhCarl Rynegardh
30119
answered Feb 23 at 20:19
Carl RynegardhCarl Rynegardh
30119
30119
$begingroup$
I already do SVD for big data analysis in other course and i had challange with this stuff , all reduction (PCA,SVD, MDS, TSNE , ecc) but in this course is mandatory take a dataset and use a PCA and data visual (in D3). The professor link reduction with visualizzation , but i dont understand how
$endgroup$
– theantomc
Feb 24 at 8:59
add a comment |
$begingroup$
I already do SVD for big data analysis in other course and i had challange with this stuff , all reduction (PCA,SVD, MDS, TSNE , ecc) but in this course is mandatory take a dataset and use a PCA and data visual (in D3). The professor link reduction with visualizzation , but i dont understand how
$endgroup$
– theantomc
Feb 24 at 8:59
$begingroup$
I already do SVD for big data analysis in other course and i had challange with this stuff , all reduction (PCA,SVD, MDS, TSNE , ecc) but in this course is mandatory take a dataset and use a PCA and data visual (in D3). The professor link reduction with visualizzation , but i dont understand how
$endgroup$
– theantomc
Feb 24 at 8:59
$begingroup$
I already do SVD for big data analysis in other course and i had challange with this stuff , all reduction (PCA,SVD, MDS, TSNE , ecc) but in this course is mandatory take a dataset and use a PCA and data visual (in D3). The professor link reduction with visualizzation , but i dont understand how
$endgroup$
– theantomc
Feb 24 at 8:59
add a comment |
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