informationloss

Information loss refers to a decline in the amount of information content or usable information about a system as a result of processing, measurement or transmission. It occurs when a process discards, distorts, or aggregates data in a way that prevents exact recovery of the original state.

In information theory, loss occurs when the mapping from input to output is not information-preserving. For a deterministic function, non-injective mappings merge multiple inputs into the same output, making the original input unrecoverable. The amount of loss can be analyzed using entropy and mutual information; a reduction in mutual information between input and output or an increase in conditional entropy H(X|Y) signals information loss.

Common sources include lossy data compression, quantization, sampling, feature extraction, and measurement noise. In data transmission, channel noise and distortion cause information loss; in signal processing, dimensionality reduction may discard details.

In physics, information loss is discussed in contexts such as irreversible thermodynamic processes and the black hole information paradox, where whether information about a system's initial state is preserved under evolution is debated.

Mitigation depends on goals: lossless compression and reversible transformations preserve information; error-correcting codes and higher sampling rates reduce practical loss; in machine learning, retaining sufficient statistics minimizes functional information loss.