Glyphboard: Visual Exploration of High-Dimensional Data Combining Glyphs with Dimensionality Reduction.

Rigorous data science is interdisciplinary at its core. In order to make sense of high-dimensional data, data scientists need to enter into a dialogue with domain experts. We present Glyphboard, a visualization tool that aims to support this dialogue.

Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations.

This work studies the problem of learning appropriate low dimensional image representations. We propose a generic algorithmic framework, which leverages two classic representation learning paradigms, i.e.

Model-based learning of local image features for unsupervised texture segmentation.

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images.

Reconstructible Nonlinear Dimensionality Reduction via Joint Dictionary Learning.

This paper presents a parametric low-dimensional (LD) representation learning method that allows to reconstruct high-dimensional (HD) input vectors in an unsupervised manner. Under the assumption that the HD data and its LD representation share the same or similar local sparse structure, the proposed method achieves reconstructible dimensionality reduction via jointly learning dictionaries in both the original HD data space and its LD representation space. By regarding the sparse representation as a smooth function with respect to a specific dictionary, we construct an encoding-decoding block for learning LD representations from sparse coefficients of HD data.

Dynamical Textures Modeling via Joint Video Dictionary Learning.

Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DTs) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process.

Analysis operator learning and its application to image reconstruction.

Exploiting a priori known structural information lies at the core of many image reconstruction methods that can be stated as inverse problems. The synthesis model, which assumes that images can be decomposed into a linear combination of very few atoms of some dictionary, is now a well established tool for the design of image reconstruction algorithms. An interesting alternative is the analysis model, where the signal is multiplied by an analysis operator and the outcome is assumed to be sparse.

Local convergence analysis of FastICA and related algorithms.

The FastICA algorithm is one of the most prominent methods to solve the problem of linear independent component analysis (ICA). Although there have been several attempts to prove local convergence properties of FastICA, rigorous analysis is still missing in the community. The major difficulty of analysis is because of the well-known sign-flipping phenomenon of FastICA, which causes the discontinuity of the corresponding FastICA map on the unit sphere.