[jira] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen updated SPARK-11530: -- Priority: Minor (was: Major) > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis >Assignee: Sean Owen >Priority: Minor > Fix For: 2.0.0 > > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng updated SPARK-11530: -- Shepherd: Xiangrui Meng (was: Sean Owen) Assignee: Sean Owen > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis >Assignee: Sean Owen > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Christos Iraklis Tsatsoulis updated SPARK-11530: Component/s: MLlib > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen updated SPARK-11530: -- Shepherd: Sean Owen Go for it. I started a little bit on it but please continue. I will shepherd/review > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Christos Iraklis Tsatsoulis updated SPARK-11530: Description: For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix. Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use. For details, see these SO questions http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ (pyspark) http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ was: For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix. Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use. See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ for details. > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > For details, see these SO questions > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > (pyspark) > http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala) > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Christos Iraklis Tsatsoulis updated SPARK-11530: Description: For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix. Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use. See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ for details. was: For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix. Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use. See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/) and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ for details. > Return eigenvalues with PCA model > - > > Key: SPARK-11530 > URL: https://issues.apache.org/jira/browse/SPARK-11530 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.5.1 >Reporter: Christos Iraklis Tsatsoulis > > For data scientists & statisticians, PCA is of little use if they cannot > estimate the _proportion of variance explained_ by selecting _k_ principal > components (see here for the math details: > https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section > 'Explained variance'). To estimate this, one only needs the eigenvalues of > the covariance matrix. > Although the eigenvalues are currently computed during PCA model fitting, > they are not _returned_; hence, as it stands now, PCA in Spark ML is of > extremely limited practical use. > See this SO question > http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ > > and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/ > for details. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
