Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification
نویسندگان
چکیده
Plant has plenty use in foodstuff, medicine and industry, and is also vitally important for environmental protection. So, it is important and urgent to recognize and classify plant species. Plant classification based on leaf images is a basic research of botanical area and agricultural production. Due to the high nature complexity and high dimensionality of leaf image data, dimensional reduction algorithms are useful and necessary for such type of data analysis, since it can facilitate fast classifying plants, and understanding and managing plant leaf features. Supervised locally linear embedding (SLLE) is a powerful feature extraction method, which can yield very promising recognition results when coupled with some simple classifiers. In this paper, a semi-SLLE is proposed and is applied to plant classification based on leaf images. The experiment results show that the proposed algorithm performs very well on leaf image data which exhibits a manifold structure.
منابع مشابه
Semi-supervised learning with Gaussian fields
Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. This paper presents two contributions. First, we show how the GF framework can be used for regression tasks on high-dimensional data. We consider an active learning strategy based on entropy minimization and a maximum likelihood model selection method. Second, we show h...
متن کاملFeature dimension reduction for microarray data analysis using locally linear embedding
Cancer classification is one major application of microarray data analysis. Due to the ultra high dimensionality nature of microarray data, data dimension reduction has drawn special attention for such type of data analysis. The currently available data dimension reduction methods are either supervised, where data need to be labeled, or computational complex. In this paper, we proposed to use a...
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 کاملClassification Constrained Dimensionality Reduction
Dimensionality reduction is a topic of recent interest. In this paper, we present the classification constrained dimensionality reduction (CCDR) algorithm to account for label information. The algorithm can account for multiple classes as well as the semi-supervised setting. We present an out-of-sample expressions for both labeled and unlabeled data. For unlabeled data, we introduce a method of...
متن کاملSupervised 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 کامل