Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear DIMENSIONALity reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.
This paper studies the problem of categorical data clustering,
especially for transactional data characterized by high
DIMENSIONALity and large volume. Starting from a heuristic method
of increasing the height-to-width ratio of the cluster histogram, we
develop a novel algorithm – CLOPE, which is very fast and
scalable, while being quite effective. We demonstrate the
performance of our algorithm on two real world
Feature selection is a preprocessing technique frequently used in data mining and machine learning tasks. It can reduce DIMENSIONALity, remove irrelevant data, increase learning accuracy, and improve results comprehensibility. FCBF is a fast correlation-based filter algorithm designed for high-dimensional data and has been shown effective in removing both irrelevant features and redundant features
This thesis presents a comprehensive overview of the problem of facial recognition. A survey of available facial detection algorithms as well as implementation and tests of di鏗€erent feature extraction and DIMENSIONALity reduction methods and light normalization methods are presented.
Multiuser multiple-input-multiple-output (MU-
MIMO) systems are known to be hindered by DIMENSIONALity
loss due to channel state information (CSI) acquisition overhead.
In this paper, we investigate user-scheduling in MU-MIMO
systems on account of CSI acquisition overhead, where a base
station dynamically acquires user channels to avoid choking the
system with CSI overhead.
