When only a small number of labeled samples are available, supervised dimensionality
reduction methods tend to perform poorly because of overfitting. In such cases, unlabeled …

This paper gives an overview of the eigenvalue problems encountered in areas of data
mining that are related to dimension reduction. Given some input high‐dimensional data, the …

Trace ratio is a natural criterion in discriminant analysis as it directly connects to the
Euclidean distances between training data points. This criterion is re-analyzed in this paper …

Now a days hyperspectral image processing is gaining attraction to the researchers
because of its ability to detect and recognize the unique land cover types with high accuracy …

Even though 1 in 6 men in the US, in their lifetime are expected to be diagnosed with
prostate cancer (CaP), only 1 in 37 is expected to die on account of it. Consequently, among …

Semisupervised dimensionality reduction has been attracting much attention as it not only
utilizes both labeled and unlabeled data simultaneously, but also works well in the situation …

Compressive sensing is an emerging field predicated upon the fact that, if a signal has a
sparse representation in some basis, then it can be almost exactly reconstructed from very …

We present a general framework of semi-supervised dimensionality reduction for manifold
learning which naturally generalizes existing supervised and unsupervised learning …

In this paper, we propose a method for matching biometric data from disparate domains.
Specifically, we focus on the problem of comparing a low-resolution (LR) image with a high …

Local Fisher discriminant analysis (LFDA) is a feature extraction technique that proved
efficient to reduce several types of data and succeeded to outperform many state-of-the-art …