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» Forecasting high-dimensional data
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ICDM
2008
IEEE
146views Data Mining» more  ICDM 2008»
15 years 4 months ago
Isolation Forest
Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies...
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou
ISCAS
2008
IEEE
145views Hardware» more  ISCAS 2008»
15 years 4 months ago
Group learning using contrast NMF : Application to functional and structural MRI of schizophrenia
— Non-negative Matrix factorization (NMF) has increasingly been used as a tool in signal processing in the last couple of years. NMF, like independent component analysis (ICA) is...
Vamsi K. Potluru, Vince D. Calhoun
GECCO
2007
Springer
153views Optimization» more  GECCO 2007»
15 years 4 months ago
Parallel genetic algorithm: assessment of performance in multidimensional scaling
Visualization of multidimensional data by means of Multidimensional Scaling (MDS) is a popular technique of exploratory data analysis widely usable, e.g. in analysis of bio-medica...
Antanas Zilinskas, Julius Zilinskas
ICML
2004
IEEE
15 years 3 months ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
PAKDD
2004
ACM
97views Data Mining» more  PAKDD 2004»
15 years 3 months ago
Further Applications of a Particle Visualization Framework
Our previous work introduced a 3D particle visualization framework that viewed each data point as being a particle affected by gravitational forces. We showed the use of this tool ...
Ke Yin, Ian Davidson