Independent Component Analysis (ICA) is a statistical method for expressing an observed set of random vectors as a linear combination of statistically independent components. This...
Hariton Korizis, Nikolaos Mitianoudis, Anthony G. ...
—This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing...
Time-frequency representations constitute the main tool for analysis of nonstationary signals arising in real-life systems. One of the most challenging applications of time-frequen...
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
Recently, several manifold learning algorithms have been proposed, such as ISOMAP (Tenenbaum et al., 2000), Locally Linear Embedding (Roweis & Saul, 2000), Laplacian Eigenmap ...