Dimensionality reduction techniques seek to represent a set of images as a set of points in a low dimensional space. Here we explore a video representation that considers a video as two parts – a space of possible images and a trajectory through that space. The nonlinear dimensionality reduction technique of Isomap, gives, for many interesting scenes, a very low dimensional representation of th...

In this paper we address the problem of automatically deriving vocabularies of motion modules from human motion data, taking advantage of the underlying structure in motion. We approach this problem with a data-driven methodology for modularizing a motion stream (or time-series of human motion) into a vocabulary of parameterized actions and a set of high-level behaviors for sequencing actions. ...

In this paper we consider the analysis of thousands of unorganized , low resolution images of an object. With very low resolution images, standard computer vision techniques offinding corresponding points and solving for image warping parameters or 3D geometry may fail. Two recent techniques in statistical pattern recognition, locally linear embedding (LLE) and Isomap, give a mechanism for find...

Word spotting is an effective paradigm for indexing document images with minimal human effort. Here, the use of the Bag-ofFeatures principle has been shown to achieve competitive results on different benchmarks. Recently, a spatial pyramid approach was used as a word image representation to improve the retrieval results even further. The high dimensionality of the spatial pyramids was attempted...

In this paper, we study how a multidimensional local binary pattern (LBP) texture feature data can be visually explored and analyzed. The goal is to determine how true paper properties can be characterized with local texture features from visible light images. We utilize isometric feature mapping (Isomap) for the LBP texture feature data and perform non-linear dimensionality reduction for the d...

Scientific and engineering processes produce massive high-dimensional data sets that are generated as highly non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold can facilitate better understanding of the underlying process, and ultimately its optimization. We show that off-the-shelf non-linear spectral dimensionality methods...

The growing importance of diffusion tensor imaging (DTI) in studying the white matter architecture in normal and pathologic states necessitates the development of tools for comprehensive analysis of diffusion tensor data. Operations such as multivariate statistical analysis and hypothesis testing, interpolation and filtering, must now be performed on tensor data, and must overcome challenges in...

Keywords: Classifiers Datasets No free lunch theorem PCA Isomap a b s t r a c t Given the posterior probability estimates of 14 classifiers on 38 datasets, we plot two-dimensional maps of classifiers and datasets using principal component analysis (PCA) and Isomap. The similarity between classifiers indicate correlation (or diversity) between them and can be used in deciding whether to include ...

A good curriculum is crucial for a successful university education. When developing a curriculum, topics, such as natural science, informatics, etc. are set first, and then course syllabi are written accordingly. However, there is no guarantee that the topics actually covered by the course syllabi are identical to the initially set topics. To find out if the actual topics covered by the develop...

We use a Spectral Clustering model to formulate a distributed implementation using SPARK of Laplacian Eigenmaps that we call Distributed Spectral Dimensionality Reduction (DSDR). We evaluate DSDR to visualize conceptual clusters of terms in textual data from 2149 short documents written by online contributors to a State Department website. We compare DSDR with PCA, MultiDimensional Scaling, ISO...