The current update method for the LinearRegression estimator relies on the Sherman-Morrison formula to update the (inverse) covariance matrix for a single new example. This is computationally provident, but limits the use of the method to single-example updates.
It would be great to also accept multiple examples (e.g., arrays of dimension NxM rather than just 1xM) using the Woodbury matrix identity. This will be computationally more expensive as it will require a matrix inversion, but potentially more valuable for certain use-cases.
ddbourgin
commented
3 years ago
The current
updatemethod for the LinearRegression estimator relies on the Sherman-Morrison formula to update the (inverse) covariance matrix for a single new example. This is computationally provident, but limits the use of the method to single-example updates.It would be great to also accept multiple examples (e.g., arrays of dimension NxM rather than just 1xM) using the Woodbury matrix identity. This will be computationally more expensive as it will require a matrix inversion, but potentially more valuable for certain use-cases.