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Found 10 papers, 0 papers with code
Global Positioning: the Uniqueness Question and a New Solution Method
We fill a gap in the literature by giving a proof for the long-held belief that when $m \ge n+2$, the solution is unique for almost all user positions.
Optimality and complexity of classification by random projection
In particular, our bounds imply that, unless the number of projections n is extremely large, there is a significant advantageous gap between the generalization error of the random projection approach and that of a linear classifier in the extended space.
Clustering small datasets in high-dimension by random projection
The different structures uncovered are found to persist as more points are added to the dataset.
A highly likely clusterable data model with no clusters
We propose a model for a dataset in ${\mathbb R}^D$ that does not contain any clusters but yet is such that a projection of the points on a random one-dimensional subspace is likely to yield a clustering of the points.
Three Efficient, Low-Complexity Algorithms for Automatic Color Trapping
The trapping method they follow is based on a hardware-friendly technique proposed by J. Trask (JTHBCT03) which is too computationally expensive for software or firmware implementation.
Photo-unrealistic Image Enhancement for Subject Placement in Outdoor Photography
Camera display reflections are an issue in bright light situations, as they may prevent users from correctly positioning the subject in the picture.
A Hand-Held Multimedia Translation and Interpretation System with Application to Diet Management
The memory requirements of the application, including the database of images, are also well within the limits of the device.
Pattern Dependence Detection using n-TARP Clustering
We propose a method to quantify and validate the dependencies of the outcome random variable on the various patterns contained in the observed random variable.
Benchmarks for Image Classification and Other High-dimensional Pattern Recognition Problems
We propose classification benchmarks based on simple random projection heuristics.
The Pascal Triangle of a Discrete Image: Definition, Properties and Application to Shape Analysis
We define the Pascal triangle of a discrete (gray scale) image as a pyramidal arrangement of complex-valued moments and we explore its geometric significance.
