We identify a new and interesting high-dimensional outlier detection problem in this paper, that is, detecting the subspaces in which given data points are outliers. We call the subspaces in which a data point is an outlier as its Outlying Subspaces. In this paper, we will propose the prototype of a dynamic subspace search system, called HOS-Miner (HOS stands for High-dimensional Outlying Subsp...

‎In wireless communication systems‎, ‎space-time codes are applied to encode data when multiple antennas are used in the receiver and transmitter‎. ‎The concept of diversity is very crucial in designing space-time codes‎. ‎In this paper‎, ‎using the equivalent definition of full diversity space-time codes‎, ‎we first characterize all real and complex $2times 2$ rate one linear dispersion space-...

The different methods of reducing decoherence in quantum devices are discussed from the unified point of view based on the energy conservation principle and the concept of forbidden transitions. Minimal decoherence model, " bang-bang " techniques, Zeno effect and decoherence-free subspaces are studied as particular examples.

The efficiency of quantum computing relies on the fact that the behavior of quantum mechanical systems follows a different logic than the behavior of classical systems. However, it is in general difficult to find physical systems well decoupled from the environment, which might destroy their quantumness. An important task in quantum information science is therefore the engineering of systems wi...

This paper concerns the closed points, closed subspaces, open subspaces, weakly closed and weakly open subspaces, and effective divisors, on a non-commutative space.

In [13] frames of subspaces extended to continuous version namely c-frame of subspaces. In this article we consider to the relations between cframes of subspaces and local c-frames. Also in this article we give some important relation about duality and parseval c-frames of subspaces.

I argue that the spectra in a hyperspectral datacube will usually lie in several low-dimensional subspaces, and that these subspaces are more easily estimated from the data than the endmembers. I present an algorithm for finding the subspaces. The algorithm fits the data with a Gaussian mixture model, in which the means and covariance matrices are parameterized in terms of the subspaces. The lo...