ABSTRACT
PURPOSE: To evaluate the interexaminer and intraexaminer reliability of macular microperimetry using the microperimeter MP-1. METHODS PARTICIPANTS: Fifteen healthy volunteers younger than 40 years of age (Group 1), 15 healthy subjects over 60 years (Group 2), and five patients with age-related macular degeneration (Group 3). OBSERVATION PROCEDURE: Two examiners (E1 and E2) measured, in random order, interexaminer (E2-E1a) reliability. Another examination was undergone by one of the examiners a week later to evaluate the intraexaminer (E1b-E1a) reliability. MAIN OUTCOME MEASURES: Macular sensitivity (mean threshold (decibel)) and stability of fixation were determined using MP1 microperimetry. Agreement was analysed by means of Bland-Altman plots and by the determination of the intraclass correlation coefficient.ResultsThe interexaminer (E2-E1a) and the intraexaminer (E1b-E1a) differences in the mean threshold values were not statistically significant (P=0.850, 95% confidence Interval (CI)=-0.265 to 0.319; P=0.246, 95% CI=-0.099 to 0.375, respectively). Limits of agreement and intraclass correlation coefficients also showed good agreement in each group. CONCLUSIONS: A good reliability was found for the mean threshold values in all the three groups, indicating examiner-independent measurements.
Subject(s)
Fixation, Ocular/physiology , Macular Degeneration/physiopathology , Retina/physiology , Visual Field Tests/instrumentation , Visual Fields/physiology , Aged , Aged, 80 and over , Analysis of Variance , Female , Humans , Male , Middle Aged , Observer Variation , Visual Acuity/physiology , Visual Field Tests/methodsABSTRACT
Within the last years various principal component analysis (PCA) algorithms have been proposed. In this paper we use a general framework to describe those PCA algorithms which are based on Hebbian learning. For an important subset of these algorithms, the local algorithms, we fully describe their equilibria, where all lateral connections are set to zero and their local stability. We show how the parameters in the PCA algorithms have to be chosen in order to get an algorithm which converges to a stable equilibrium which provides principal component extraction.
ABSTRACT
In this paper we consider the principal component analysis (PCA) and vector quantization (VQ) neural networks for image compression. We present a method where the PCA and VQ steps are adaptively combined. A learning algorithm for this combined network is derived. We demonstrate that this approach can improve the results of the successive application of the individually optimal methods.
