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- Wei‐Ying MaSuggested from co-authorship
- Jiawei HanSuggested from co-authorship
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Works304 from public data
- Laplacian Score for Feature Selection2,330
This paper proposes a "filter" method for feature selection which is independent of any learning algorithm, based on the observation that, in many real world classification problems, data from the same class are often close to each other.
- Unsupervised feature selection for multi-cluster data1,149
Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, a new approach is proposed, called Multi-Cluster Feature Selection (MCFS), for unsupervised feature selection, which select those features such that the multi-cluster structure of the data can be best preserved.
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- VIPS: a Vision-based Page Segmentation Algorithm698
An automatic top-down, tag-tree independent approach to detect web content structure that simulates how a user understands web layout structure based on his visual perception.
- 631
- Fast approximate nearest neighbor search with the navigating spreading-out graph626
A novel graph structure called Monotonic Relative Neighborhood Graph (MRNG) is proposed which guarantees very low search complexity (close to logarithmic time) and is proposed to further lower the indexing complexity and make it practical for billion-node ANNS problems.
- 578
- Constrained Nonnegative Matrix Factorization for Image Representation520
It is shown how explicitly combining label information improves the discriminating power of the resulting matrix decomposition, and the effectiveness of the novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real-world applications.
- What to Do Next: Modeling User Behaviors by Time-LSTM510
Experimental results on two real-world datasets show the superiority of the recommendation method using Time-LSTM over the traditional methods.
- PandaGPT: One Model To Instruction-Follow Them All467
PandaGPT serves as an initial step toward building AGI that can perceive and understand inputs in different modalities holistically, as the authors humans do and is presented as an approach to emPower large lANguage moDels with visual and Auditory instruction-following capabilities.
- Locality sensitive discriminant analysis465
A novel linear algorithm for discriminant analysis, called Locality Sensitive Discriminant Analysis (LSDA), which finds a projection which maximizes the margin between data points from different classes at each local area by discovering the local manifold structure.
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- Extracting Content Structure for Web Pages Based on Visual Representation426
This paper presents an automatic top-down, tag-tree independent approach to detect web content structure that simulates how a user understands web layout structure based on his visual perception.
- Large Scale Spectral Clustering with Landmark-Based Representation424
This paper proposes a novel approach, called Landmark-based Spectral Clustering (LSC), for large scale clustering problems, where the original data points are represented as the linear combinations of landmarks and the spectral embedding of the data can be efficiently computed with the landmark-based representation.
- Topic modeling with network regularization414
A novel solution to the problem of topic modeling with network structure (TMN) is proposed, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data.
- Tensor Subspace Analysis407
A new algorithm called Tensor Subspace Analysis (TSA) is proposed that detects the intrinsic local geometrical structure of the tensor space by learning a lower dimensional tensor subspace and achieves better recognition rate, while being much more efficient.
- 399
- Self-taught hashing for fast similarity search395
This paper proposes a novel Self-Taught Hashing (STH) approach to semantic hashing: it first finds the optimal l-bit binary codes for all documents in the given corpus via unsupervised learning, and then train l classifiers via supervised learning to predict the l- bit code for any query document unseen before.
- Hierarchical clustering of WWW image search results using visual, textual and link information389
A hierarchical clustering method using visual, textual and link analysis to cluster the search results into different semantic clusters of image search results is proposed.
- Hierarchical Clustering of WWW Image Search Results Using Visual389
A hierarchical clustering method using visual, textual and link analysis to cluster the search results into different semantic clusters of image search results is proposed.
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- CLRNet: Cross Layer Refinement Network for Lane Detection329
This work presents Cross Layer Refinement Network (CLRNet), a novel network design which first detects lanes with high-level semantic features then performs refinement based on low-level features and introduces Line IoU loss which regresses the lane line as a whole unit to improve the localization accuracy.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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