Presented By: DCMB Seminar Series
Department of Computational Medicine & Bioinformatics Weekly Seminar
Robert M. Haralick (City University of New York), "Subspace Classifiers"
Abstract:
Subspace classifiers have been around for a long time, beginning with feature selection, which in essence was a subspace selection technique. This talk will discuss the kind of subspace classifiers that Bledsoe and Browning presented in their 1959 paper and from which there have been a variety of extensions which we will discuss.
The Bledsoe and Browning subspace classifier quantizes measurement space. Each quantized observation tuple corresponds to a cell in measurement space. A collection of subspaces are selected at random. In the original form the subspaces were mutually exclusive. For each class, each cell of a subspace contained a number dependent on the number of observations of the training data that fell into that cell. For each class those numbers were combined in ways not dissimilar to random forests. For a given observation tuple, the class with the highest vote count was selected as the assigned class.
We will discuss a variety of principled extensions of the technique and make some comparisons with Neural Networks.
Research Interests:
High-dimensional space clustering, pattern recognition, knowledge discovery and artificial intelligence
Professor Haralick began his work as one of the principal investigators of the NASA ERTS satellite data doing remote sensing image analysis.
He has made a series of contributions in the field of computer vision. In the high-level vision area, he has worked on inferring 3D geometry from one or more perspective projection views.] He has also identified a variety of vision problems which are special cases of the consistent labeling problem. His papers on consistent labeling, arrangements, relation homomorphism, matching, and tree search translate some specific computer vision problems to the more general combinatorial consistent labeling problem and then discuss the theory of the look-ahead operators that speed up the tree search. The most basic of these is called Forward Checking. This gives a framework for the control structure required in high-level vision problems. He has also extended the forward-checking tree search technique to propositional logic.
Zoom: https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09
Subspace classifiers have been around for a long time, beginning with feature selection, which in essence was a subspace selection technique. This talk will discuss the kind of subspace classifiers that Bledsoe and Browning presented in their 1959 paper and from which there have been a variety of extensions which we will discuss.
The Bledsoe and Browning subspace classifier quantizes measurement space. Each quantized observation tuple corresponds to a cell in measurement space. A collection of subspaces are selected at random. In the original form the subspaces were mutually exclusive. For each class, each cell of a subspace contained a number dependent on the number of observations of the training data that fell into that cell. For each class those numbers were combined in ways not dissimilar to random forests. For a given observation tuple, the class with the highest vote count was selected as the assigned class.
We will discuss a variety of principled extensions of the technique and make some comparisons with Neural Networks.
Research Interests:
High-dimensional space clustering, pattern recognition, knowledge discovery and artificial intelligence
Professor Haralick began his work as one of the principal investigators of the NASA ERTS satellite data doing remote sensing image analysis.
He has made a series of contributions in the field of computer vision. In the high-level vision area, he has worked on inferring 3D geometry from one or more perspective projection views.] He has also identified a variety of vision problems which are special cases of the consistent labeling problem. His papers on consistent labeling, arrangements, relation homomorphism, matching, and tree search translate some specific computer vision problems to the more general combinatorial consistent labeling problem and then discuss the theory of the look-ahead operators that speed up the tree search. The most basic of these is called Forward Checking. This gives a framework for the control structure required in high-level vision problems. He has also extended the forward-checking tree search technique to propositional logic.
Zoom: https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09
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