Due to the physiological constraints of articulatory motion the speech apparatus has limited degrees of freedom. As a result, the range of speech sounds a human is capable of producing may lie on a low dimensional submanifold of the high dimensional space of all possible sounds. In this study a number of manifold learning algorithms are applied to speech data in an effort to extract useful low ...

Manifold learning methods have widely used in ordinary image processing domain. It has many advantages, depending on the different formulation of the manifold. Hyperspectral images are kind of images acquired by air-borne or space-born platforms. This paper introduces a novel manifold learning based dimension reduction (DR) method for hyperspectral classification. The purpose is to fully utiliz...

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Most real data sets contain atypical observations, often referred to as outliers. Their presence may have a negative impact in data modeling using machine learning. This is particularly the case in data density estimation approaches. Manifold learning techniques provide low-dimensional data representations, often oriented towards visualization. The visualization provided by density estimation m...

Nonlinear dimensionality reduction: Alternative ordination approaches for extracting and visualizing biodiversity patterns in tropical montane forest vegetation data Miguel D. Mahecha⁎, Alfredo Martínez, Gunnar Lischeid, Erwin Beck Ecological Modelling, Bayreuth Centre for Ecology and Ecosystem Research BayCEER, University of Bayreuth, 95440 Bayreuth, Germany Max Planck Institute for Biogeochem...

We present a novel non-iterative and rigorously motivated approach for estimating hidden Markov models (HMMs) and factorial hidden Markov models (FHMMs) of high-dimensional signals. Our approach utilizes the asymptotic properties of a spectral, graph-based approach for dimensionality reduction and manifold learning, namely the diffusion framework. We exemplify our approach by applying it to the...