source code for SDSPCAAN
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Updated
Dec 7, 2020 - MATLAB
source code for SDSPCAAN
The code for prototype selection and instance ranking using matrix decomposition and subspace learning
We use self-expressive layer to learn the affinity matrix of the hidden space of a generator, and we found subspace in GAN. Also we propsed a subspace-based high-fidelity GAN inversion model.
Data Augmented Flatness-aware Gradient Projection for Continual Learning. ICCV, 2023.
A novel dynamic learning strategy that overcomes the empirical search of an optimal number of subspace learners in multiple metric learners.
Streaming, Memory-Limited, r-truncated SVD Revisited!
We propose a global and local feature transformation method for PRID. The global feature transformation matrix projects the data from different cameras to a common space. We further hypothesize that a latent basis matrix can be learnt in this space which represents the shared structure between different cameras using matrix factorization.
We implement some sparse representation based face recognition algorithms here, including LRC, SRC, CRC, ESRC and etc.
Subspace Support Vector Data Description
source code for MvBLS paper
Code for the ICLR2022 paper on Subspace Regularization for few-shot class incremental image classification
[IEEE TSP 2021] “Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee”. IEEE Transactions on Signal Processing, 2021.
Ellipsoidal Subspace Support Vector Data Description
models/scripts for subspace learning written in MATLAB (2018 Yau Award CS Bronze)
Multimodal Subspace Support Vector Data Description
Dual Shared-Specific Multiview Subspace Clustering
Matlab implementation of L0 motivated low-rank sparse subspace clustering
MATLAB implementation of "Federated Over-Air Subspace Tracking from Incomplete and Corrupted Data", IEEE Transactions on Signal Processing, Jun 2022.
MATLAB implementation of "Nearly Optimal Robust Subspace Tracking", ICML 2018. Longer version to appear in IEEE Journal of Selected Areas in Information Theory, 2020.
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