This node applies a projection to the principal components on the given
input data. The data model of the PCA
computation node can be applied to arbitrary data
to reduce it to a given number of dimensions.
The information preservation rates in the selection of the target dimensions give the expected
approximation rates based on the training data fed into the connected PCA Compute
node. These rates assume that data fed into the predictor is equally distributed
as the data the PCA was computed for initially.
Options
Popular Predecessors
- PCA Compute30 %
- Normalizer6 %
- PCA2 %
- Normalizer Apply (PMML)2 %
- Column Filter2 %
- k-Means2 %
- Missing Value2 %
CSV Reader2 %Column Rename1 %- HiLite Row Splitter1 %
- Table View (JavaScript)1 %
- Concatenate1 %
- Number to String1 %
- Normalizer (Apply)1 %
Table Row to Variable1 %Joiner1 %- File Reader1 %
- Expand Bit Vector< 1 %
- Partitioning< 1 %
- Data Generator< 1 %
- Math Formula< 1 %
- PCA Inversion< 1 %
- Missing Value Column Filter< 1 %
- CSV Reader< 1 %
- Model Reader< 1 %
Column Appender< 1 %Linear Correlation< 1 %- Correlation Filter< 1 %
- Column Splitter< 1 %
- Table Transposer< 1 %
- Color Manager< 1 %
- Fingerprints Expander< 1 %
- Category to Number (Apply)< 1 %
- Split Collection Column< 1 %
- X-Partitioner< 1 %
- PCA Apply< 1 %
- String to Number< 1 %
- One to Many< 1 %
- Reference Column Filter< 1 %
Row Splitter (deprecated)< 1 %Row Filter (deprecated)< 1 %- GroupBy< 1 %
- Low Variance Filter< 1 %
- Normalizer (PMML)< 1 %
- Rule-based Row Filter< 1 %
- Numeric Outliers< 1 %
Color Appender< 1 %- Math Formula (Multi Column)< 1 %
Excel Reader (XLS)< 1 %- Scatter Plot (JavaScript)< 1 %
- RDKit Descriptor Calculation< 1 %
- Constant Value Column Filter< 1 %
- Joiner< 1 %
- Excel Reader< 1 %
- Double Configuration< 1 %
- Data Explorer< 1 %
- Pivot< 1 %
- Variable Creator< 1 %
- Duplicate Row Filter< 1 %
File Reader< 1 %- Lag Column< 1 %
- Double to Integer< 1 %
- Missing Value (Apply)< 1 %
- RowID< 1 %
- Linear Discriminant Analysis< 1 %
Excel Reader (XLS)< 1 %Python Script (1⇒1)< 1 %- ARFF Reader< 1 %
- Linear Regression Learner< 1 %
- Ungroup< 1 %
- One-way ANOVA< 1 %
- Distance Matrix Calculate< 1 %
Table Column to Variable< 1 %- Constant Value Column< 1 %
- Row Sampling< 1 %
- Rule Engine< 1 %
- Shape Manager< 1 %
- Column Expressions< 1 %
- Integer Configuration< 1 %
Extract Date&Time Fields< 1 %- Table Reader< 1 %
- Reference Row Filter< 1 %
- Scatter Plot (Plotly)< 1 %
Table Row To Variable Loop Start< 1 %Model Reader< 1 %Table Writer< 1 %- Java Snippet< 1 %
Counting Loop Start< 1 %- Fuzzy c-Means< 1 %
- Logistic Regression Learner< 1 %
- Logistic Regression Predictor< 1 %
- Numeric Scorer< 1 %
- Expand Byte Vector< 1 %
- Column Auto Type Cast< 1 %
- Category to Number< 1 %
- Column Resorter< 1 %
- Cross Joiner< 1 %
- Domain Calculator< 1 %
Rank Correlation< 1 %- Linear Discriminant Analysis Compute< 1 %
- Extract Table Dimension< 1 %
- DB Query Reader< 1 %
- 3D Scatter Plot (Plotly)< 1 %
- Document Vector< 1 %
- Double Widget< 1 %
- Parameter Optimization Loop Start< 1 %
- Molecular Properties< 1 %
- Substructure Search< 1 %
- RDKit Fingerprint< 1 %
- Table Creator< 1 %
- Loop End< 1 %
- Interval Loop Start< 1 %
- Table Row to Variable Loop Start< 1 %
- Naive Bayes Predictor< 1 %
- Cluster Assigner< 1 %
- DBSCAN< 1 %
- Column Appender< 1 %
- Row Filter (Labs)< 1 %
- Reference Row Splitter< 1 %
- Denormalizer< 1 %
- Cache< 1 %
- Column Filter (legacy)< 1 %
- Value Filter (legacy)< 1 %
- Python Script (legacy)< 1 %
- R Source (Table)< 1 %
- Date&Time-based Row Filter< 1 %
Views
- This node has no views
Workflows
(3D) Fragment library design workflowKNIME Hub- 01_AnalyticsKNIME Hub
- 02_NIR_Spectral_Data_Analysis_Clustering_and_VisualizationKNIME Hub
- 02_NIR_Spectral_Data_Analysis_Clustering_and_VisualizationKNIME Hub
- 02_NIR_Spectral_Data_Analysis_Clustering_and_VisualizationKNIME Hub
- Show all 86 workflows
Links
- No links available
Developers
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