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Known Limitations

  • transform(new_data) is not implemented. Only training scores are returned. To project new data, refit on the combined dataset.

  • inverse_transform() always returns dense output, even when the input was sparse CSR/CSC.

  • MissingAwareSparseOneHotEncoder requires numeric categories. String categories cannot survive the CSR round-trip.

  • Data convention. AutoEncoder expects samples × features; VBPCA expects features × samples. Transpose as needed.

  • RMS oscillation with uncentered data. When bias=True (the default) and the input data has non-zero feature means, the RMS convergence trace can exhibit a stable period-2 oscillation caused by a one-iteration lag between the mean update and the reconstruction error.

    Workaround: center your data before fitting — use MissingAwareStandardScaler (or AutoEncoder) as a preprocessing step. Pre-centered data eliminates the oscillation entirely, even with bias=True.