Abstract As basic research, it has also received increasing attention from people that the “curse of dimensionality” will lead to increase cost data storage and computing; influences efficiency accuracy dealing with problems. Feature dimensionality reduction as a key link in process pattern recognition become one hot difficulty spot field recognition, machine learning mining. It is most challen...

In this paper, we analyze the behavior of the multivariate symmetric uncertainty (MSU) measure through the use of statistical simulation techniques under various mixes of informative and non-informative randomly generated features. Experiments show how the number of attributes, their cardinalities, and the sample size affect the MSU. We discovered a condition that preserves good quality in the ...

ion methods for dimensionality-reduction Abstractions methods are used to complement iterative algorithms for computing equilibria. Abstractions are usually created algorithmically, by utilizing domain-dependent structure to set up a manageable optimization problem that produces a smaller game which retains as much of the original game structure as possible. No reasonable bounds on solution qua...

Categorical attributes are those that can take a discrete set of values, e.g., colours. This work is about compressing vectors over categorical to low-dimension vectors. The current hash-based methods do not provide any guarantee on the Hamming distances between compressed representations. Here we present FSketch create sketches for sparse data and an estimator estimate pairwise among uncompres...