The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural computation. Here we describe locally linear embedding (LLE), an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data. The data, assum...

Lindera obtusiloba has been used in traditional herbal medicine for the treatment of blood stasis and inflammation. The leaves of Lindera obtusiloba have been reported to exhibit various physiological activities. However, there is little information available on their antiplatelet and antithrombotic activities. Thus, the present study aimed to evaluate the effect of Lindera obtusiloba leaf extr...

In order to evaluate the effects of short exposure period using a simple methodology, a plain rectangular approximation method to estimate loss of life expectancy (LLE) was devised and applied to evaluate cancer risk. The posed risk was expressed as the time of LLE, which is proportional to the period and concentration of exposure. This method assumes a rectangular survival curve and a flat can...

The main purpose of this work was the preliminary qualitative study of organic compounds in wastewaters of swine slaughterhouses. The samples were collected in a local abattoir and submitted to Liquid-Liquid Extraction (LLE) and Solid-phase Extraction (SPE) with XAD-4 resin as stationary phase. The instrumental analysis was performed by Gas Chromatography with Mass Spectrometer Detector (GC/MSD...

Given the nonlinear manifold structure of facial images, a new kernel-based supervised manifold learning algorithm based on locally linear embedding (LLE), called discriminant kernel locally linear embedding (DKLLE), is proposed for facial expression recognition. The proposed DKLLE aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the...

Spectral manifold learning techniques have recently found extensive applications in machine vision. The common strategy of spectral algorithms for manifold learning is exploiting the local relationships in a symmetric adjacency graph, which is typically constructed using -nearest neighborhood ( -NN) criterion. In this paper, with our focus on locally linear embedding as a powerful and well-know...

The material handling systems are one of the key components of the most modern manufacturing systems. The sensory signals of material handling systems are nonlinear and have unique characteristics. It is very difficult to encode and classify these signals by using multipurpose methods. In this study, performances of multiple generic methods were studied for the diagnostic of the pneumatic syste...

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...

* The author was supported by the János Bolyai fellowship of the Hungarian Academy of Sciences. Abstract – Many problems in machine learning are hard to manage without applying some pre-processing or feature extraction method. Two popular forms of dimensionality reduction are the methods of principal component analysis (PCA) [2] and multidimensional scaling (MDS) [18]. In this paper we examine ...