LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Solution of the matrix equation AX + XB = C [F4]
Communications of the ACM
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Benefitting from the variables that variable selection discards
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Journal of Machine Learning Research
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
The VLDB Journal — The International Journal on Very Large Data Bases
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Unsupervised feature selection for principal components analysis
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Locality condensation: a new dimensionality reduction method for image retrieval
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
Estimating Optimal Feature Subsets Using Mutual Information Feature Selector and Rough Sets
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Stable local dimensionality reduction approaches
Pattern Recognition
Nonlinear Dimensionality Reduction with Local Spline Embedding
IEEE Transactions on Knowledge and Data Engineering
General Cost Models for Evaluating Dimensionality Reduction in High-Dimensional Spaces
IEEE Transactions on Knowledge and Data Engineering
Exponential family sparse coding with applications to self-taught learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Bregman Divergence-Based Regularization for Transfer Subspace Learning
IEEE Transactions on Knowledge and Data Engineering
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Towards Structural Sparsity: An Explicit l2/l0 Approach
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
IEEE Transactions on Multimedia
l2,1-norm regularized discriminative feature selection for unsupervised learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A "nonnegative PCA" algorithm for independent component analysis
IEEE Transactions on Neural Networks
Graph Regularized Sparse Coding for Image Representation
IEEE Transactions on Image Processing
Large Margin Subspace Learning for feature selection
Pattern Recognition
Hybrid structure for robust dimensionality reduction
Neurocomputing
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To build an effective dimensionality reduction model usually requires sufficient data. Otherwise, traditional dimensionality reduction methods might be less effective. However, sufficient data cannot always be guaranteed in real applications. In this paper we focus on performing unsupervised dimensionality reduction on the high-dimensional and small-sized data, in which the dimensionality of target data is high and the number of target data is small. To handle the problem, we propose a novel Self-taught Dimensionality Reduction (STDR) approach, which is able to transfer external knowledge (or information) from freely available external (or auxiliary) data to the high-dimensional and small-sized target data. The proposed STDR consists of three steps: First, the bases are learnt from sufficient external data, which might come from the same ''type'' or ''modality'' of target data. The bases are the common part between external data and target data, i.e., the external knowledge (or information). Second, target data are reconstructed by the learnt bases by proposing a novel joint graph sparse coding model, which not only provides robust reconstruction ability but also preserves the local structures amongst target data in the original space. This process transfers the external knowledge (i.e., the learnt bases) to target data. Moreover, the proposed solver to the proposed model is theoretically guaranteed that the objective function of the proposed model converges to the global optimum. After this, target data are mapped into the learnt basis space, and are sparsely represented by the bases, i.e., represented by parts of the bases. Third, the sparse features (that is, the rows with zero (or small) values) of the new representations of target data are deleted for achieving the effectiveness and the efficiency. That is, this step performs feature selection on the new representations of target data. Finally, experimental results at various types of datasets show the proposed STDR outperforms the state-of-the-art algorithms in terms of k-means clustering performance.