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m2e

code for paper Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis

mcgc

TIP 2019: Multiview Consensus Graph Clustering

mcles

Multi-view Clustering in Latent Embedding Space, AAAI2020

mlrdsc

Code release for "Multi-Level Representation Learning for Deep Subspace Clustering" (WACV 2020)

mm18-deep-cognitive-subspace-clustering

Tensorflow implementation for Deep Cognitive Subspace Clustering (DeepCogSC)

multi-view

multi-view-datasets

Multi-view datasets for classification or clustering

multi_view-learning-summarize

I am doing some research about multi-view learning and I want to make a summarize about my work.

multiview-learning

multiview_learning

mvdata

Data sets for multi-view learning.

mvdscn

:game_die: The official tensorflow implemention of the paper for "Multi-view Deep Subspace Clustering Networks"

mvgl

TCyb17: Graph learning for multiview clustering

myblog

a jekyll powered blog theme specially designed to take notes, not just blogs

nrlpapers

Must-read papers on network representation learning (NRL) / network embedding (NE)

refacing

resnet-cppn-gan-tensorflow

Using Residual Generative Adversarial Networks and Variational Auto-encoder techniques to produce high resolution images.

rmkkm

Robust Multiple Kernel K-means using L21-norm

robust-subspace-segmentation-by-low-rank-representation

scal

This repository contains code and experiments for Subspace Clustering with Active Learning (SCAL).

simultaneous-representation-learning-and-clustering-for-incomplete-multi-view-data

sm2sc

The implementation of Split Multiplicative Multi-view Subspace Clustering, to appear in T-IP

spatialattentiongan

SaGAN PyTorch "Generative Adversarial Network with Spatial Attention for Face Attribute Editing"

subspace-clustering

Toolbox for large scale subspace clustering

tai_v3h

Code for TAI 2021 paper: V3H: View Variation and View Heredity for Incomplete Multi-view Clustering

tetci_uimc

Code for IEEE TETCI 2021 paper: Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

the-gan-zoo

transdim

Machine learning for transportation data imputation and prediction.

transfer-spectral-clustering

Source code for transfer spectral clustering (TSC)

truncated-cauchy-non-negative-matrix-factorization

Non-negative matrix factorization (NMF) minimizes the euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large errors, and develop a Truncated CauchyNMF to robustly learn the subspace on noisy datasets contaminated by outliers. We theoretically analyze the robustness of Truncated CauchyNMF comparing with the competing models and theoretically prove that Truncated CauchyNMF has a generalization bound which converges at a rate of order O(lnn/n‾‾‾‾‾√) , where n is the sample size. We evaluate Truncated CauchyNMF by image clustering on both simulated and real datasets. The experimental results on the datasets containing gross corruptions validate the effectiveness and robustness of Truncated CauchyNMF for learning robust subspaces.