On selecting the best features in a noisy environment

summary:This paper introduces a novel method for selecting a feature subset yielding an optimal trade-off between class separability and feature space dimensionality. We assume the following feature properties: (a) the features are ordered into a sequence, (b) robustness of the features decreases with an increasing order and (c) higher-order features supply more detailed information about the objects. We present a general algorithm how to find under those assumptions the optimal feature subset. Its performance is demonstrated experimentally in the space of moment-based descriptors of 1-D signals, which are invariant to linear filtering

Invariants for Recognition of Degraded 1-D Digital Signals

The paper is devoted to the feature-based recognition of degraded signals acquired by a linear time-invariant system. The proposed approach consists of describing signals by features which are invariant with respect to the degradation and recognizing signals in the feature space. Neither impulse response identification nor signal restoration are required. Thanks to this, the new approach is in many applications much more effective than the classical one based on blind deconvolution. Two sets of appropriate blur-invariant features (the first one in time domain, the other one in spectral domain) are introduced in this paper. The proof of their invariance is given and their performance is illustrated by numerical experiments. 1. Introduction One of very frequent tasks in digital signal processing is the classification of 1-D finite (i.e. time-limited) signals (experimental curves) with respect to the template curves stored in a database. This task appears in EEG and ECG processing, speec..