This contains some papers with respect to classification, clustering and etc.
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Ultra-Scalable Spectral Clustering and Ensemble Clustering | TKDE | Code |
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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 |
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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 |
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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 |
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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 |
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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 |
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clustering and projected clustering with adaptive neighbors | KDD | Code |
constructing robust affinity graphs for spectral clustering | CVPR | Code |
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spectral rotation versus K-means in spectral clustering | AAAI | Code |
large-scale spectral clustering on graphs | AAAI | Code |
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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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fast approximate spectral clustering | KDD | Code |
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on spectral clustering-analysis and algorithm | NIPS | Code |
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