01209nas a2200181   4500008004100000245005600041210005500097260000900152300001200161490000700173520060600180653002900786653002100815653004500836653001600881100001300897856011700910       2014                        eng d00aTensor graph-optimized linear discriminant analysis0 aTensor graphoptimized linear discriminant analysis  c2014  a31 - 350 v123 aGraph-based Fisher Analysis (GbFA) is proposed recently for dimensionality reduction, which has the powerful discriminant ability. However, GbFA is based on the matrix-to-vector way, which not only costs much but also loses spatial relations of pixels in images. Therefore, Tensor Graph-based Linear Discriminant Analysis (TGbLDA) is proposed in the paper. TGbLDA regards samples as data in tensor space and gets projection matrixes through the iteration way. Besides, TGbLDA inherits merits of GbFA. Experiments on Yale and YaleB face datasets demonstrate the effectiveness of our proposed algorithm.10aDimensionality reduction10aFace recognition10aGraph-based fisher discriminant analysis10aTensor data1 aChen, J.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84900431732&partnerID=40&md5=cbccd93685522d83cd7e46df5728b79701388nas a2200181   4500008004100000245006200041210006200103260000900165300001400174490000700188520072900195653002900924653003900953653003000992653005601022100001101078856011701089       2013                        eng d00aRegularized orthogonal local fisher discriminant analysis0 aRegularized orthogonal local fisher discriminant analysis  c2013  a154 - 1590 v113 aAiming at deficiencies of the ability for preserving local nonlinear structure of recently proposed Regularized Orthogonal Linear Discriminant Analysis (ROLDA) for dimensionality reduction, a kind of dimensionality reduction algorithm named Regularized Orthogonal Local Fisher Discriminant Analysis (ROLFDA) is proposed in the paper, which is originated from ROLDA. The algorithm introduce the idea of local structure preserving in Local Fisher Discriminant Analysis (LFDA) on the basic of ROLDA, following properties of ROLDA and strengthening the ability for capturing local structure information of data with nonlinear structures. Experiments on real face datasets demonstrate the effectiveness of our proposed algorithm.10aDimensionality reduction10aLocal fisher discriminant analysis10aLocal nonlinear structure10aRegularized orthogonal linear discriminant analysis1 aXu, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879121480&partnerID=40&md5=294ec31f3b8631180e347dff9cae6b7802444nas a2200217   4500008004100000245007500041210006900116260000900185300001400194490000600208520172600214653002901940653002801969653002201997653002702019653001602046100001602062700001502078700001602093856011702109       2008                        eng d00aHybrid clustering approach for term partitioning in document data sets0 aHybrid clustering approach for term partitioning in document dat  c2008  a272 - 2770 v63 aInformation retrieval is one of the major research areas due to accumulation of huge information in digital form. Various techniques of Information retrieval are based on the fact that terms contained in a document along with their frequency of occurrence signify the semantics of the document. Recent attempts to find the relevant document for a context represents documents in a vector space model as document-term vector containing term weights for every index term in that document. As there will be enormous number of index terms this leads to high dimensionality problem. We can reduce the dimensionality based on the observation that groups of terms associated with related concepts occur together or do not occur in a document based on whether the document is relevant or not to that concept. Such a group of terms Is identified as an equivalence class and can be viewed as a single dimension in a Rough set based information retrieval system. In this paper we present a hybrid clustering approach for the formation of equivalence classes of terms associated with related concepts. It uses the outcome of hierarchical clustering to provide seed points for implementing Incremental K-means algorithm. Due to the sparsity of the term vector the cosine similarity estimate was found to be ineffective for term clustering. Another promising measure of proximity estimate used in information retrieval namely Euclidian distance has a drawback that It is biased towards changes in the term frequencies in larger documents when the term weights are represented by tf-ldf estimates. Hence we propose normalization for tf-idf estimates while representing a term as a vector in a document space before clustering the terms.10aDimensionality reduction10aHierarchical clustering10aK means algorithm10aPartitional clustering10aText mining1 aReddy, K.T.1 aShashi, M.1 aReddy, L.P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350735762&partnerID=40&md5=911fe260f9a7d869bf7ee8660851cdac