First-principles calculations based on density functional theory, both with the local density approximation (LDA) and with generalized gradient corrections (GGA), have been used to simulate solid and liquid MgO in direct coexistence in the range of pressure 0 < or = p < or = 135 GPa. The calculated LDA zero pressure melting temperature is T(LDA)m = 3110 +/- 50 K, in good agreement with the expe...

In this paper, a new statistical projection-based method called Two-DimensionalOriented Linear Discriminant Analysis (2DO-LDA) is presented. While in the Fisherfaces method the 2D image matrices are first transformed into 1D vectors by merging their rows of pixels, 2DO-LDA is directly applied on matrices, as 2D-PCA. Within and between-class image covariance matrices are generalized, and 2DO-LDA...

Clustering Web services that groups together services with similar functionalities helps improve both the accuracy and efficiency of the Web service search engines. An important limitation of existing Web service clustering approaches is that they solely focus on utilizing WSDL (Web Service Description Language) documents. There has been a recent trend of using user-contributed tagging data to ...

Crop leaf disease management and control pose significant impact on enhancement in yield quality to fulfill consumer needs. For smart agriculture, an intelligent identification system is inevitable for efficient crop health monitoring. In this view, a novel approach proposed using feature fusion PCA-LDA classification (FF-PCA-LDA). Handcrafted hybrid deep features are extracted from RGB images....

This paper proposes an innovative algorithm named 2D-LDA, which directly extracts the proper features from image matrices based on Fisher s Linear Discriminant Analysis. We experimentally compare 2D-LDA to other feature extraction methods, such as 2D-PCA, Eigenfaces and Fisherfaces. And 2D-LDA achieves the best performance. 2004 Elsevier B.V. All rights reserved.

The clinical notes in a given patient record contain much redundancy, in large part due to clinicians' documentation habit of copying from previous notes in the record and pasting into a new note. Previous work has shown that this redundancy has a negative impact on the quality of text mining and topic modeling in particular. In this paper we describe a novel variant of Latent Dirichlet Allocat...

This paper presents the development of Linear Discriminant Analysis toolkit (LDA-Toolkit) and its integration into widely used COST249 SpeechDat(II) Task Force Reference Recognizer (RefRec). The crucial parts of the LDA, the determination of LDA classes, as well as the influence of the level of dimensionality reduction on automatic speech recognition performance, are discussed. Evaluation of pr...

Latent topic analysis has emerged as one of the most effective methods for classifying, clustering and retrieving textual data. However, existing models such as Latent Dirichlet Allocation (LDA) were developed for static corpora of relatively large documents. In contrast, much of the textual content on the web, and especially social media, is temporally sequenced, and comes in short fragments s...