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LDA is used to carve up multidimensional space.

PCA is used to collapse multidimensional space.

PCA allows the collapsing of hundreds of spatial dimensions into a handful of lower spatial dimensions while preserving 70% - 90% of the important information. 3D objects cast 2D shadows. We can see the shape of an object from it's shadow. But we can't know everything about the shape from a single shadow. By having a small collection of shadows from different (globally optimal) angles, then we can know most things about the shape of an object. PCA helps reduce the 'Curse of Dimensionality' when modelling.

LDA is for classification, it almost always outperforms Logistic Regression when modelling small data with well separated clusters. It also handles multi-class data and class imbalances.

LDA is used to carve up multidimensional space.

PCA is used to collapse multidimensional space.

PCA allows the collapsing of hundreds of spatial dimensions into a handful of lower spatial dimensions while usually preserving 70% - 90% of the important information.

PCA: 3D objects cast 2D shadows. We can see the shape of an object from it's shadow. But we can't know everything about the shape from a single shadow. By having a small collection of shadows from different (globally optimal) angles, then we can know most things about the shape and size of an object. PCA helps reduce the 'Curse of Dimensionality' when modelling.

LDA is for classification, it almost always outperforms Logistic Regression when modelling small data with well separated clusters. It's also good at handling multi-class data and class imbalances.

LDA is used to carve up multidimensional space.

PCA is used to collapse multidimensional space.

PCA allows the collapsing of hundreds of spatial dimensions into a handful of lower spatial dimensions while preserving 70% - 90% of the important information. 3D objects cast 2D shadows. We can see the shape of an object from it's shadow. But we can't know everything about the shape from a single shadow. By having a small collection of shadows from different (globally optimal) angles, then we can know most things about the shape of an object. PCA can help reduce the 'Curse of Dimensionality' when modelling.

LDA is for classification, it almost always outperforms Logistic Regression when modelling small data with well separated clusters. It also handles multi-class data and class imbalances.

LDA is used to carve up multidimensional space.

PCA is used to collapse multidimensional space.

PCA allows the collapsing of hundreds of spatial dimensions into a handful of lower spatial dimensions while preserving 70% - 90% of the important information. 3D objects cast 2D shadows. We can see the shape of an object from it's shadow. But we can't know everything about the shape from a single shadow. By having a small collection of shadows from different (globally optimal) angles, then we can know most things about the shape of an object. PCA helps reduce the 'Curse of Dimensionality' when modelling.

LDA is for classification, it almost always outperforms Logistic Regression when modelling small data with well separated clusters. It also handles multi-class data and class imbalances.

LDA is used to carve up multidimensional space.

PCA is used to collapse multidimensional space.

PCA allows the collapsing of hundreds of spatial dimensions into a handful of lower spatial dimensions while preserving 70% - 90% of the important information. 3D objects cast 2D shadows. We can see the shape of an object from it's shadow. But we can't know everything about the shape from a single shadow. By having a small collection of shadows from different (globally optimal) angles, then we can know most things about the shape of an object. PCA can help reduce the 'Curse of Dimensionality' when modelling.

LDA is for classification, it almost always outperforms Logistic Regression when modelling small data with well separated clusters. It also performs well on multi-class data and class imbalances.

LDA is used to carve up multidimensional space.

PCA is used to collapse multidimensional space.

PCA allows the collapsing of hundreds of spatial dimensions into a handful of lower spatial dimensions while preserving 70% - 90% of the important information. 3D objects cast 2D shadows. We can see the shape of an object from it's shadow. But we can't know everything about the shape from a single shadow. By having a small collection of shadows from different (globally optimal) angles, then we can know most things about the shape of an object. PCA can help reduce the 'Curse of Dimensionality' when modelling.

LDA is for classification, it almost always outperforms Logistic Regression when modelling small data with well separated clusters. It also handles multi-class data and class imbalances.