3- https://doi.org/10.1007/11556121_4
NMF with LogGabor Wavelets for Visualization
- Jan 1, 2005
- Zhonglong Zheng +2 more
Abstract
Many problems in image representation and classification involve some form of dimensionality reduction. Non-negative matrix factorization (NMF) is a recently proposed unsupervised procedure for learning spatially localized, parts-based subspace representation of objects. Here we present an improvement of the classical NMF by combining with Log-Gabor wavelets to enhance its part-based learning ability. In addition, we compare the new method with principal component analysis (PCA) and locally linear embedding (LLE) proposed recently in Science. Finally, we apply the new method to several real world datasets and achieve good performance in representation and classification.
- # Non-negative Matrix Factorization
- # Classical Non-negative Matrix Factorization
- # Form Of Dimensionality Reduction
- # Log-Gabor Wavelets
- # Locally Linear Embedding
- # Real World Datasets
- # Good Performance In Classification
- # Problems In Image Classification
- # Image Representation
- # Principal Component Analysis
