Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation

Representation learning is currently a very hot topic in modern machine learning, mostly due to the great success of the deep learning methods. In particular low-dimensional representation which discriminates classes can not only enhance the classification procedure, but also make it faster, while contrary to the high-dimensional embeddings can be efficiently used for visual based exploratory data analysis. In this paper we propose Maximum Entropy Linear Manifold (MELM), a multidimensional generalization of Multithreshold Entropy Linear Classifier model which is able to find a low-dimensional linear data projection maximizing discriminativeness of projected classes. As a result we obtain a linear embedding which can be used for classification, class aware dimensionality reduction and data visualization. MELM provides highly discriminative 2D projections of the data which can be used as a method for constructing robust classifiers. We provide both empirical evaluation as well as some interesting theoretical properties of our objective function such us scale and affine transformation invariance, connections with PCA and bounding of the expected balanced accuracy error.Comment: submitted to ECMLPKDD 201

Teachers' affective domain and transformation in team-based learning

Teachers engaged in innovative professional learning and applied action research to investigate and understand pedagogical or school-focussed elements that they considered were problematic for their teaching practice or student learning. Along with the new skills and knowledge they acquired to inform their practice, the professional learning experience was a catalyst for multidimensional perspective transformation and the transformative learning some teachers realised. This qualitative, interpretive research problematised the phenomenon of personal transformation occurring for some teachers and explored a central research question: What are teachers’ conceptual understandings and affect concerning any transformative learning following a team-based learning experience? Teachers’ stories reveal the affective associations teachers made and are described across the full spectrum of human emotion from fear to joy in their attitudes, values and beliefs, and motivations and transformative learning arising from their professional learning experience. The teachers’ transformative learning experiences unfolded in unique ways and revealed the relationships between action research for professional learning, affective dispositions, and transformative learning. Teachers described a sense of self and shared an overwhelming sense of empowerment from their personal growth and professional achievement attributed to their action research. The evolving theories of transformative learning inform my understanding and interpretation of teachers’ transformative learning and its relation to affect. This research contributes evidence to understand transformative learning through the lens of teachers’ professional learning in teams-based action research. It reveals teachers’ transformative professional learning can occur spontaneously in socially, supported situations created for team-based learning. The research has implications for teachers’ professional learning in the future

Blur-Robust Face Recognition via Transformation Learning

Abstract. This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred im-ages to a same feature subspace by the method of multidimensional s-caling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the mani-fold structure of the original shape images and, at the same time, en-hances the class separability, resulting in a wide applications to various descriptors. Furthermore, we combine our method with subspace-based point spread function (PSF) estimation method to handle cases of un-known blur degree, by applying the feature transformation correspond-ing to the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance a-gainst the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.

On the Schoenberg Transformations in Data Analysis: Theory and Illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A simple distance-based discriminant algorithm illustrates the theory, intimately connected to the Gaussian kernels of Machine Learning

Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks

New machine learning based algorithms have been developed and tested for Monte Carlo integration based on generative Boosted Decision Trees and Deep Neural Networks. Both of these algorithms exhibit substantial improvements compared to existing algorithms for non-factorizable integrands in terms of the achievable integration precision for a given number of target function evaluations. Large scale Monte Carlo generation of complex collider physics processes with improved efficiency can be achieved by implementing these algorithms into commonly used matrix element Monte Carlo generators once their robustness is demonstrated and performance validated for the relevant classes of matrix elements