76 citations · 310 across the 22 of their papers we have counts for
9 papers · 1 filter
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral, probabilistic, and deep metric learning. We first start with the definition of distanc…
Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on the Johnson-Lindenstrauss (JL) lemma and linear and nonlinear random projections. We start with linear random projection and then justify its…
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then…
Generative Locally Linear Embedding
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embed…
Locally Linear Embedding and its Variants: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper for Locally Linear Embedding (LLE) and its variants. The idea of LLE is fitting the local structure of manifold in the embedding space. In this…
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
Multidimensional Scaling (MDS) is one of the first fundamental manifold learning methods. It can be categorized into several methods, i.e., classical MDS, kernel classical MDS, met…