Dimension Reduction by Mutual Information Discriminant Analysis

In the past few decades, researchers have proposed many discriminant analysis (DA) algorithms for the study of high-dimensional data in a variety of problems. Most DA algorithms for feature extraction are based on transformations that simultaneously maximize the between-class scatter and minimize the withinclass scatter matrices. This paper presents a novel DA algorithm for feature extraction using mutual information (MI). However, it is not always easy to obtain an accurate estimation for high-dimensional MI. In this paper, we propose an efficient method for feature extraction that is based on one-dimensional MI estimations. We will refer to this algorithm as mutual information discriminant analysis (MIDA). The performance of this proposed method was evaluated using UCI databases. The results indicate that MIDA provides robust performance over different data sets with different characteristics and that MIDA always performs better than, or at least comparable to, the best performing algorithms.Comment: 13pages, 3 tables, International Journal of Artificial Intelligence & Application

Automated extraction of absorption features from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Geophysical and Environmental Research Imaging Spectrometer (GERIS) data

Automated techniques were developed for the extraction and characterization of absorption features from reflectance spectra. The absorption feature extraction algorithms were successfully tested on laboratory, field, and aircraft imaging spectrometer data. A suite of laboratory spectra of the most common minerals was analyzed and absorption band characteristics tabulated. A prototype expert system was designed, implemented, and successfully tested to allow identification of minerals based on the extracted absorption band characteristics. AVIRIS spectra for a site in the northern Grapevine Mountains, Nevada, have been characterized and the minerals sericite (fine grained muscovite) and dolomite were identified. The minerals kaolinite, alunite, and buddingtonite were identified and mapped for a site at Cuprite, Nevada, using the feature extraction algorithms on the new Geophysical and Environmental Research 64 channel imaging spectrometer (GERIS) data. The feature extraction routines (written in FORTRAN and C) were interfaced to the expert system (written in PROLOG) to allow both efficient processing of numerical data and logical spectrum analysis

Matrix decomposition algorithms for feature extraction

Clinical decision support software is a delicate system which, can potentially be the physician’s closest friend. The aim of such systems is to be able to cleverly recommend a list of treatment options which closely matches the patient. We envisage a system which learns from experts without ever needing to ask them for feedback, and thus one which learns from past patient encounters. The system needs to be adaptive as well as dynamic, since all patients are different even if they may exhibit very similar symptoms. This paper proposes using matrices to capture such data, and algorithms using Singular Value Decomposition to predict treatments.peer-reviewe

Randomized Dimensionality Reduction for k-means Clustering

We study the topic of dimensionality reduction for $k$-means clustering. Dimensionality reduction encompasses the union of two approaches: \emph{feature selection} and \emph{feature extraction}. A feature selection based algorithm for $k$-means clustering selects a small subset of the input features and then applies $k$-means clustering on the selected features. A feature extraction based algorithm for $k$-means clustering constructs a small set of new artificial features and then applies $k$-means clustering on the constructed features. Despite the significance of $k$-means clustering as well as the wealth of heuristic methods addressing it, provably accurate feature selection methods for $k$-means clustering are not known. On the other hand, two provably accurate feature extraction methods for $k$-means clustering are known in the literature; one is based on random projections and the other is based on the singular value decomposition (SVD). This paper makes further progress towards a better understanding of dimensionality reduction for $k$-means clustering. Namely, we present the first provably accurate feature selection method for $k$-means clustering and, in addition, we present two feature extraction methods. The first feature extraction method is based on random projections and it improves upon the existing results in terms of time complexity and number of features needed to be extracted. The second feature extraction method is based on fast approximate SVD factorizations and it also improves upon the existing results in terms of time complexity. The proposed algorithms are randomized and provide constant-factor approximation guarantees with respect to the optimal $k$-means objective value.Comment: IEEE Transactions on Information Theory, to appea

An investigation of data compression techniques for hyperspectral core imager data

We investigate algorithms for tractable analysis of real hyperspectral image data from core samples provided by AngloGold Ashanti. In particular, we investigate feature extraction, non-linear dimension reduction using diffusion maps and wavelet approximation methods on our data