Manifold learning based techniques have been found to be useful for feature space transformations and semi-supervised learning in speech processing. However, the immense computational requirements in building neighborhood graphs have hindered the application of these techniques to large speech corpora. This paper presents an approach for fast computation of neighborhood graphs in the context of...

Manifold Learning (ML) is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus, ML algorithms are most applicable to highdimensional data and require large sample sizes to accurately estimate the manifold. Despite this, most existing manifold learning implementations are not particularly scalable. Here we present a Python package that implemen...

Manifold Learning (ML) is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus ML algorithms are, at least in theory, most applicable to high-dimensional data and sample sizes to enable accurate estimation of the manifold. Despite this, most existing manifold learning implementations are not particularly scalable. Here we present a Python pack...

Manifold learning is an effective dimensional reduction technique for face feature extraction, which, generally speaking, tends to preserve the local neighborhood structures of given samples. However, neighbors of a sample often comprise more inter-class data than intra-class data, which is an undesirable effect for classification. In this paper, we address this problem by proposing a subclass-...

Symmetric Positive Definite (SPD) matrices have been widely used for data representation in many visual recognition tasks. The success mainly attributes to learning discriminative SPD matrices with encoding the Riemannian geometry of the underlying SPD manifold. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly ...

Laser-induced breakdown spectroscopy (LIBS) is currently being used on-board the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality ...

Linear dimensionality reduction plays a very important role in side channel attacks, but it is helpless when meeting the non-linear leakage of masking implementations. Increasing the order of masking makes the attack complexity grow exponentially, which makes the research of nonlinear dimensionality reduction very meaningful. However, the related work is seldom studied. A kernel function was fi...

Manifold regularization (MR) provides a powerful framework for semi-supervised classification (SSC) using both the labeled and unlabeled data. It first constructs a single Laplacian graph over the whole dataset for representing the manifold structure, and then enforces the smoothness constraint over such graph by a Laplacian regularizer in learning. However, the smoothness over such a single La...

We investigate the appearance manifold of different face poses using manifold learning. The pose estimation problem is, however, exacerbated by changes in illumination, spatial scale, etc. In addition, manifold learning has some disadvantages. First, the discriminant ability of the low-dimensional subspaces obtained by manifold learning often is lower than traditional dimesionality reduction ap...