Results 31 to 40 of about 3,322,529 (303)
New approaches on dimensionality reduction in hyperspectral images for classification purposes [PDF]
This paper presents a quasi-unsupervised methodology to detect endmembers within an hyperspectral scene and to derive a pixel-wise classification on its basis.
Rupert Mueller +7 more
core +1 more source
Adaptive Metric Dimensionality Reduction [PDF]
We study adaptive data-dependent dimensionality reduction in the context of supervised learning in general metric spaces. Our main statistical contribution is a generalization bound for Lipschitz functions in metric spaces that are doubling, or nearly doubling.
Lee-Ad Gottlieb +2 more
openaire +4 more sources
Dimensionality reduction by LPP‐L21
Locality preserving projection (LPP) is one of the most representative linear manifold learning methods and well exploits intrinsic structure of data. However, the performance of LPP remarkably degenerate in the presence of outliers.
Shujian Wang +3 more
doaj +1 more source
Factorization and regularization by dimensional reduction [PDF]
Since an old observation by Beenakker et al, the evaluation of QCD processes in dimensional reduction has repeatedly led to terms that seem to violate the QCD factorization theorem. We reconsider the example of the process gg->ttbar and show that the factorization problem can be completely resolved.
Signer, A., Stöckinger, D.
openaire +3 more sources
Dynamics of dimensional reduction
In d-dimensional unified theories that, along with gravity, contain an antisymmetric tensor field of rank s-1, preferential compactification of d-s or of s space-like dimensions is found to occur. This is the case in 11-dimensional supergravity where s = 4.
Peter G.O. FREUND, Mark A. RUBIN
openaire +1 more source
Local Feature Discriminant Projection [PDF]
In this paper, we propose a novel subspace learning algorithm called Local Feature Discriminant Projection (LFDP) for supervised dimensionality reduction of local features.
Zhen, Xiantong +3 more
core +1 more source
Neighbors-Based Graph Construction for Dimensionality Reduction
Dimensionality reduction is a fundamental task in the field of data mining and machine learning. In many scenes, examples in high-dimensional space usually lie on low-dimensional manifolds; thus, learning the low-dimensional embedding is important.
Hui Tian +3 more
doaj +1 more source
Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait [PDF]
Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis.
Small, M +15 more
core +1 more source
AN ALTERNATIVE DIMENSIONAL REDUCTION PRESCRIPTION [PDF]
We propose an alternative dimensional reduction prescription which in respect with Green functions corresponds to dropping the extra spatial coordinate. From this, we construct the dimensionally reduced Lagrangians both for scalars and fermions, discussing bosonization and supersymmetry in the particular two-dimensional case.
Edelstein Glaubach, José Daniel +3 more
openaire +3 more sources
Problem dimensionality reduction in design of optimal IIR filters [PDF]
A typical design of a digital IIR filter that is optimal in the sense of the weighted least squares criterion is performed by using a numerical optimisation procedure capable of searching for local minima of highly non-linear functions of vector ...
Tarczynski, A., A. Tarczynski
core +1 more source

