Abstract: A signal processing technique for generating separated signal components incorporates the steps of correlating basis vectors with input data to produce output vectors, applying a non-linear function to the output vectors to produce non-linearized vectors, and convolving the non-linearized vectors with the basis vectors to produce separated signal components. Each vector may comprise a set of one-dimensional channel vectors. Separated signal components are combined to form an input signal reconstruction and an anomaly signal indicating deviation of the reconstruction from the associated input signal. Basis vectors are determined by training which involves use of weighting factors referred to as basis scales. When used to scale separated signal components in the production of reconstruction vectors, basis scales have the effect of reducing a mean squared error between such vectors and associated input data used to derive them.

Abstract: A signal processing technique for generating separated signal components incorporates the steps of correlating basis vectors with input data to produce output vectors, applying a non-linear function to the output vectors to produce non-linearized vectors, and convolving the non-linearized vectors with the basis vectors to produce separated signal components. Each vector may comprise a set of one-dimensional channel vectors. Separated signal components are combined to form an input signal reconstruction and an anomaly signal indicating deviation of the reconstruction from the associated input signal. Basis vectors are determined by training which involves use of weighting factors referred to as basis scales. When used to scale separated signal components in the production of reconstruction vectors, basis scales have the effect of reducing a mean squared error between such vectors and associated input data used to derive them.