Results 41 to 50 of about 14,028 (258)

Affine Subspace Representation for Feature Description [PDF]

, 2014
To Appear in the 2014 European Conference on Computer ...
Zhenhua Wang 0002, Bin Fan 0001, Fuchao Wu   +2 more
openaire   +2 more sources

Exciton Radiative Lifetimes in Hexagonal Diamond Ge and SixGe1–x Alloys

Advanced Optical Materials, EarlyView.
Strong room‐temperature photoluminescence reported in hexagonal Ge conflicts with theory predicting a nearly dark band edge. First‐principles calculations of excitonic radiative lifetimes fill a key gap in this debate, showing that pristine hexagonal Ge remains intrinsically weakly emissive, while Si alloying only modestly shortens the lifetime and ...
Michele Re Fiorentin, Michele Amato, Maurizia Palummo   +2 more
wiley   +1 more source

Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this article, two CR-based dynamic ensemble selection (DES) methods using multiview kernel collaborative subspace ...
Hongliang Lu, Hongjun Su, Jun Hu, Qian Du   +3 more
doaj   +1 more source

Invariant subspaces of the Lawrence–Krammer representation [PDF]

Proceedings of the American Mathematical Society, 2011
The Lawrence–Krammer representation was used in 2000 2000 to show the linearity of the braid group.
openaire   +3 more sources

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection

Remote Sensing
As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of ...
Yulei Wang, Haipeng Ma, Yuchao Yang, Enyu Zhao, Meiping Song, Chunyan Yu   +5 more
doaj   +1 more source

Algorithm using supervised subspace learning and non‐local representation for pose variation recognition

IET Computer Vision, 2020
Pose variation has been one of the challenges of face recognition. To solve this challenge, the authors propose a classification algorithm using supervised subspace learning and non‐local representation (SSLNR).
Mengmeng Liao, Changzhi Wang, Xiaodong Gu   +2 more
doaj   +1 more source

Clustering disjoint subspaces via sparse representation [PDF]

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Given a set of data points drawn from multiple low-dimensional linear subspaces of a high-dimensional space, we consider the problem of clustering these points according to the subspaces they belong to. Our approach exploits the fact that each data point can be written as a sparse linear combination of all the other points.
Ehsan Elhamifar, René Vidal
openaire   +1 more source

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source

Multi-Scale Deep Subspace Clustering With Discriminative Learning

IEEE Access, 2022
Deep subspace clustering methods have achieved impressive clustering performance compared with other clustering algorithms. However, most existing methods suffer from the following problems: 1) they only consider the global features but neglect the local
Jiao Wang, Bin Wu, Zhenwen Ren, Yunhui Zhou   +3 more
doaj   +1 more source