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Neighborhood preserving embedding
- Xiaofei He, Deng Cai, Shuicheng Yan, HongJiang Zhang
- Computer ScienceTenth IEEE International Conference on Computer…
- 17 October 2005
This paper proposes a novel subspace learning algorithm called neighborhood preserving embedding (NPE), which aims at preserving the local neighborhood structure on the data manifold and is less sensitive to outliers than principal component analysis (PCA).
Laplacian Score for Feature Selection
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 data
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.
Semi-supervised Discriminant Analysis
- Deng Cai, Xiaofei He, Jiawei Han
- Computer ScienceIEEE 11th International Conference on Computer…
- 26 December 2007
This paper proposes a novel method, called Semi- supervised Discriminant Analysis (SDA), which makes use of both labeled and unlabeled samples to learn a discriminant function which is as smooth as possible on the data manifold.
Locality Sensitive Discriminant Analysis
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.
Orthogonal Laplacianfaces for Face Recognition
- Deng Cai, Xiaofei He, Jiawei Han, HongJiang Zhang
- Computer ScienceIEEE Transactions on Image Processing
- 1 November 2006
An appearance-based face recognition method, called orthogonal Laplacianface, based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold.
VIPS: a Vision-based Page Segmentation Algorithm
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.
Self-taught hashing for fast similarity search
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.
Graph Regularized Sparse Coding for Image Representation
A graph based algorithm, called graph regularized sparse coding, is proposed, to learn the sparse representations that explicitly take into account the local manifold structure of the data.
Graph Regularized Nonnegative Matrix Factorization for Data Representation.
- Deng Cai, Xiaofei He, Jiawei Han, Thomas S. Huang
- Computer ScienceIEEE transactions on pattern analysis and machine…
- 1 August 2011
In GNMF, an affinity graph is constructed to encode the geometrical information and a matrix factorization is sought, which respects the graph structure, and the empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.