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- Partha NiyogiSuggested from co-authorship
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Works306 from public data
- Locality Preserving Projections4,741
These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold.
- 2,450
- 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.
- Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization829
This paper proposes to achieve a better approximation to the rank of matrix by truncated nuclear norm, which is given by the nuclear norm subtracted by the sum of the largest few singular values, and develops a novel matrix completion algorithm by minimizing the Truncated Nuclear Norm.
- 805
- 758
- 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.
- 465
- 436
- 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
- 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.
- 382
- 378
- 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.
- Music recommendation by unified hypergraph327
A novel music recommendation algorithm is proposed by using both multiple kinds of social media information and music acoustic-based content, and uses hypergraph to model the various objects and relations, and considers music recommendation as a ranking problem on this hypergraph.
- CrossFormer++: A Versatile Vision Transformer Hinging on Cross-Scale Attention317
A progressive group size (PGS) paradigm and an amplitude cooling layer (ACL) to alleviate the two issues that affect vision transformers’ performance, i.e., the enlarging self-attention maps and amplitude explosion are proposed.
- 257
- PI-RCNN: An Efficient Multi-Sensor 3D Object Detector with Point-Based Attentive Cont-Conv Fusion Module248
A novel fusion approach named Point-based Attentive Cont-conv Fusion(PACF) module, which fuses multi-sensor features directly on 3D points and a 3D multi-Sensor multi-task network called Pointcloud-Image RCNN(PI-RCNN as brief), which handles the image segmentation and 3D object detection tasks.
- 246
- Learning a semantic space from user's relevance feedback for image retrieval245
This work experiments with using spectral methods to infer a semantic space from user's relevance feedback, so that the system will gradually improve its retrieval performance through accumulated user interactions.
- CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation240
This paper explores the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without further training, and proposes a novel WSSS framework called CLIP-ES.
- 219
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 2026-10-14. 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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