In this paper, an extension of a dimensionality reduction algorithm called NONNEGATIVE MATRIX FACTORIZATION is presented that combines both `bag of words' data and syntactic ...
The structural health of airframes is often monitored by analysis of the frequency of occurrence matrix (FOOM) produced after each flight. Each cell in the matrix records a stres...
For most English words, dictionaries give various senses: e.g., “bank” can stand for a financial institution, shore, set, etc. Automatic selection of the sense intended in a gi...
Alexander F. Gelbukh, Grigori Sidorov, Sang-Yong H...
We address the problem of learning distance metrics using side-information in the form of groups of "similar" points. We propose to use the RCA algorithm, which is a sim...
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essen...
The structural health of airframes is often monitored by analysis of the frequency of occurrence matrix (FOOM) produced after each flight. Each cell in the matrix records a stress...
The seminal work of Hubel and Wiesel [14] and the vast amount of work that followed it prove that hierarchies of increasingly complex cells play a central role in cortical computa...
Linear Discriminant Analysis (LDA) is a popular statistical approach for dimensionality reduction. LDA captures the global geometric structure of the data by simultaneously maximi...