Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimension reduction by local principal component analysis
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
Robust locally linear embedding
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative node localization using nonlinear data projection
ACM Transactions on Sensor Networks (TOSN)
Correlation Metric for Generalized Feature Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
L1-norm projection pursuit principal component analysis
Computational Statistics & Data Analysis
Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Universal multi-dimensional scaling
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Extending metric multidimensional scaling with Bregman divergences
Pattern Recognition
Guided Locally Linear Embedding
Pattern Recognition Letters
Face Recognition Using Nearest Feature Space Embedding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A detailed investigation into low-level feature detection in spectrogram images
Pattern Recognition
Neighborhood preserving projections (NPP): a novel linear dimension reduction method
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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In recent times the dimensionality reduction technique has been widely exploited in pattern recognition and data mining. The global linear algorithms characterize the local sampling information, thereby making it superior to Principal Component Analysis (PCA). However, these algorithms are all inefficient for extracting the local data feature, which leads to incomplete learning. A new global linear algorithm is proposed in this paper, which is named Maximal Similarity Embedding (MSE). The preserving local feature of this new algorithm makes it distinct from most other methods. The MSE algorithm utilizes the Cosine Metric to describe the geometric characteristics of neighborhood and thus seeks to maximize the global similarity for dimensionality reduction. This new proposal method is robust for sparse dataset and naturally helps in avoiding the problem of small sample size cases. Extensive experiments have been performed on both synthetic and real-world images to prove the efficiency of the MSE algorithm.