ZCALayer

The deep_lvpm.layers.ZCALayer provides an alternative orthogonalisation mechanism for DLVPM. It is selected automatically when you instantiate a StructuralModel with orthogonalization="zca". Unlike FactorLayer, which performs orthogonalisation within the layer, the ZCA whitening transform is applied inside the structural model itself, and the ZCALayer only performs batch normalisation and linear projection.

Overview

After normalising inputs, the ZCALayer projects them into a latent space. The structural model then applies a ZCA (zero‑phase component analysis) whitening to the latent outputs across views. This can improve stability and interpretability in some applications.

Constructor arguments

ZCALayer(
    kernel_regularizer=None,
    epsilon=1e-6,
    momentum=0.95,
    diag_offset=1e-6,
    tot_num=None,
    ndims=1,
    run=0,
    **kwargs,
)

Important arguments include:

• kernel_regularizer – Regulariser applied to the projection layer’s weights.

• epsilon – Small constant for batch normalisation (default: 1e-6).

• momentum – Momentum for moving statistics (default: 0.95).

• diag_offset – Small value added to the diagonal of covariance matrices to ensure invertibility (default: 1e-6).

• tot_num – Total number of samples seen during training.

• ndims – Number of latent factors to extract.

Methods

• build(input_shape) – Initialises weights and variables.

• call(inputs, training=False) – Performs batch normalisation and linear projection.

• get_config() / from_config() – Standard Keras serialisation methods.

Usage

You do not normally instantiate ZCALayer directly. It is added internally by StructuralModel when orthogonalization="zca". See the StructuralModel page for details on switching between orthogonalisation strategies.