Missing values : processing with the Kohonen algorithm
نویسندگان
چکیده
We show how it is possible to use the Kohonen self-organizing algorithm to deal with data with missing values and estimate them. After a methodological reminder, we illustrate our purpose with three applications to real-world data. Nous montrons comment il est possible d’utiliser l’algorithme d’autoorganisation de Kohonen pour traiter des données avec valeurs manquantes et estimer ces dernières. Après un rappel méthodologique, nous illustrons notre propos à partir de trois applications à des données réelles.
منابع مشابه
A New Algorithm to Impute the Missing Values in the Multivariate Case
There are several methods to make inferences about the parameters of the sampling distribution when we encounter the missing values and the censored data. In this paper, through the order statistics and the projection theorem, a novel algorithm is proposed to impute the missing values in the multivariate case. Then, the performance of this method is investigated through the simulation studies. ...
متن کاملGastroenterology Dataset Clustering Using Possibilistic Kohonen Maps
Kohonen maps are an efficient mechanism in signal processing and data mining applications. However, all the existing versions and approaches of this special type of neural networks are still incapable to efficiently handle within a simple, fast, and unified framework, the imperfection of the patterns’ information elements on the one hand like the uncertainty, the missing data, etc., and the het...
متن کاملCompression of Medical Images using Improved Kohonen Algorithm
Nowadays, neural networks are largely used in signal processing and images. In particular, Kohonen networks or Self Organizing Maps are unsupervised learning models. This method performs a vector quantization (VQ) on the values obtained after processing. The vector quantization has a potential to give more data compression maintaining the same quality. In this paper we propose new scheme to ima...
متن کاملتحلیل درستنمایی ماکزیمم مدل رگرسیون لجستیک در حالتی که داده های متغیرهای پیشگو کامل نیستند ولی متغیرهای کمکی وجود دارند
Background and Objectives: Missing data exist in many studies, e.g. in regression models, and they decrease the model's efficacy. Many methods have been suggested for handling incomplete data: they have generally focused on missing outcome values. But covariate values can also be missing.Materials and Methods: In this paper we study the missing imputation by the EM algorithm and auxiliary varia...
متن کاملInitialization mechanism in Kohonen neural network implemented in CMOS technology
An initialization mechanism is presented for Kohonen neural network implemented in CMOS technology. Proper selection of initial values of neurons’ weights has a large influence on speed of the learning algorithm and finally on the quantization error of the network, which for different initial parameters can vary even by several orders of magnitude. Experiments with the software model of designe...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/math/0701152 شماره
صفحات -
تاریخ انتشار 2005