Locally linear embedding for classification
نویسندگان
چکیده
Locally linear embedding (LLE) is a recently proposed unsupervised procedure for mapping high-dimensional data nonlinearly to a lower-dimensional space. In this paper, a supervised variation on LLE is proposed. This mapping, when combined with simple classifiers such as the nearest mean classifier, is shown to yield remarkably good classification results in experiments. Furthermore, a number of algorithmic improvements are proposed which should ease application of both traditional and supervised LLE by eliminating the need for setting some of the parameters.
منابع مشابه
Supervised locally linear embedding for classification: an application to gene expression data analysis Supervised locally linear embedding in problemi di classificazione: un’applicazione all’analisi di dati di espressione genica
Some real problems, such as image recognition or the analysis of gene expression data, involve the observation of a very large number of variables on a few units. In such a context conventional classification methods are difficult to employ both from analytical and interpretative points of view. In this paper, a solution based on locally linear embedding (LLE) for supervised classification is p...
متن کاملShort term load forecast by using Locally Linear Embedding manifold learning and a hybrid RBF-Fuzzy network
The aim of the short term load forecasting is to forecast the electric power load for unit commitment, evaluating the reliability of the system, economic dispatch, and so on. Short term load forecasting obviously plays an important role in traditional non-cooperative power systems. Moreover, in a restructured power system a generator company (GENCO) should predict the system demand and its corr...
متن کاملLocally Linear Embedded Eigenspace Analysis
The existing nonlinear local methods for dimensionality reduction yield impressive results in data embedding and manifold visualization. However, they also open up the problem of how to define a unified projection from new data to the embedded subspace constructed by the training samples. Thinking globally and fitting locally, we present a new linear embedding approach, called Locally Embedded ...
متن کاملA Hybrid Business Failure Prediction Model Using Locally Linear Embedding and Support Vector Machines
The purpose of this paper is to propose a hybrid model which combines locally linear embedding (LLE) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the LLE algorithm to perform dimension reduction for feature extraction, is then utilized as a preprocessor to improve business failure prediction capability by ...
متن کاملClassification of handwritten digits using supervised locally linear embedding algorithm and support vector machine
The locally linear embedding (LLE) algorithm is an unsupervised technique recently proposed for nonlinear dimensionality reduction. In this paper, we describe its supervised variant (SLLE). This is a conceptually new method, where class membership information is used to map overlapping high dimensional data into disjoint clusters in the embedded space. In experiments, we combined it with suppor...
متن کامل