This contains some papers with respect to classification, clustering and etc.
Title |
Conference/Journal |
Code |
Ultra-Scalable Spectral Clustering and Ensemble Clustering |
TKDE |
Code |
Title |
Conference/Journal |
Code |
similarity learning via kernel preserving embedding |
AAAI |
Code |
spectral clustering of signed graphs via matrix power means |
ICML |
Code |
model-based synthetic sampling for imbalanced data |
TKDE |
Code |
K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters |
KDD |
Code |
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering |
AAAI |
Code |
Similarity Preserving Representation Learning for Time Series Clustering |
IJCAI |
Code |
Supervised Hierarchical Clustering with Exponential Linkage |
ICML |
Code |
Subspace Clustering via Good Neighbors |
TPAMI |
Code |
Title |
Conference/Journal |
Code |
unified spectral clustering with optimal graph |
AAAI |
Code |
scalable spectral clustering using random binning features |
KDD |
Code |
spectral clustering of large-scale data by directly solving normalized cut |
KDD |
Code |
understanding regularized spectral clustering via graph conductance |
NIPS |
Code |
Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering |
IEEE Transactions on Signal Processing |
Code |
Multiview clustering via adaptively weighted procrustes |
KDD |
Code |
On the Spectrum of Random Features Maps of High Dimensional Data |
ICML |
Code |
Title |
Conference/Journal |
Code |
semi-supervised feature selection via rescaled linear regression |
IJCAI |
Code |
unsupervised large graph embedding |
AAAI |
Code |
robust spectral clustering for noisy data-modeling sparse corruptions improves latent embeddings |
KDD |
Code |
twin learning for similarity and clustering: a unified kernel approach |
AAAI |
Code |
AMOS: An automated model order selection algorithm for spectral graph clustering |
ICASSP |
Code |
Multiclass Capped Lp-Norm SVM for Robust Classifications |
AAAI |
Code |
A Hierarchical Algorithm for Extreme Clustering |
KDD |
Code |
scalable normalized cut with improved spectral rotation |
IJCAI |
Code |
Title |
Conference/Journal |
Code |
the constrained laplacian rank algorithm for graph-based clustering |
AAAI |
Code |
unsupervised feature selection with structured graph optimization |
AAAI |
Code |
compressive spectral clustering |
ICML |
Code |
FUSE: Full Spectral Clustering |
KDD |
Code |
cost-sensitive boosting algorithms: do we really need them? |
Machine Learning |
Code |
Multiple Kernel k-Means Clustering with Matrix-Induced Regularization |
AAAI |
Code |
Structured Doubly Stochastic Matrix for Graph Based Clustering |
KDD |
Code |
Title |
Conference/Journal |
Code |
a new simplex sparse learning model to measure data similarity for clustering |
IJCAI |
Code |
unsupervised feature selection with adaptive structure learning |
KDD |
Code |
robust multiple kernel K-means using l21 norm |
IJCAI |
Code |
Title |
Conference/Journal |
Code |
clustering and projected clustering with adaptive neighbors |
KDD |
Code |
constructing robust affinity graphs for spectral clustering |
CVPR |
Code |
Title |
Conference/Journal |
Code |
spectral rotation versus K-means in spectral clustering |
AAAI |
Code |
large-scale spectral clustering on graphs |
AAAI |
Code |
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Conference/Journal |
Code |
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Conference/Journal |
Code |
efficient and robust feature selection via joint L21 norms minimization |
NIPS |
Code |
power iteration clustering |
ICML |
Code |
making large-scale nystrom approximation possible |
ICML |
Code |
large graph construction for scalable semi-supervised learning |
ICML |
Code |
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Conference/Journal |
Code |
fast approximate spectral clustering |
KDD |
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Conference/Journal |
Code |
on spectral clustering-analysis and algorithm |
NIPS |
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