منابع مشابه
Sparse Polynomial Mapping for Manifold Learning
Manifold learning is an approach for nonlinear dimensionality reduction and has been a hot research topic in the field of computer science. A disadvantage of manifold learning methods is, however, that there are no explicit mappings from the high-dimensional feature space to the low-dimensional representation space. It restricts the application of manifold learning methods in many practical pro...
متن کاملA Positively Curved Manifold Homeomorphic
Spaces of positive curvature play a special role in geometry. Although the class of manifolds with positive (sectional) curvature is expected to be relatively small, so far there are only a few known obstructions. Moreover, for closed simply connected manifolds these coincide with the known obstructions to nonnegative curvature which are: (1) the Betti number theorem of Gromov which asserts tha...
متن کاملInvariance for Single Curved Manifold
Recently, it has been shown that, for Lambert illumination model, solely scenes composed by developable objects with a very particular albedo distribution produce an (2D) image with isolines that are (almost) invariant to light direction change. In this work, we provide and investigate a more general framework; and we show that, in general, the requirement for such invariances is quite strong, ...
متن کاملSegmentation Informed by Manifold Learning
In many biomedical imaging applications, video sequences are captured with low resolution and low contrast challenging conditions in which to detect, segment, or track features. When image deformations have just a few underlying causes, such as continuously captured cardiac MRI without breath-holds or gating, the captured images lie on a lowdimensional, non-linear manifold. The manifold structu...
متن کاملBoundary Mapping Through Manifold Learning for Connectivity-Based Cortical Parcellation
The study of the human connectome is becoming more popular due to its potential to reveal the brain function and structure. A critical step in connectome analysis is to parcellate the cortex into coherent regions that can be used to build graphical models of connectivity. Computing an optimal parcellation is of great importance, as this stage can affect the performance of the subsequent analysi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2017
ISSN: 1041-4347
DOI: 10.1109/tkde.2017.2728790