Performing lost space: discussing an exercise in recording architectural detail with the performing body

The interior of the contemporary art space provides its users with a sterilised laboratory for the placement and experience of art. Increasingly, its bleached interior presents an a priori condition for the legitimate assignment of artworks within the complex milieu of the contemporary city. Such interiors have become an architectural typology, a predetermined homogenous non-place within which artworks reside. In this sense we can look to Lefebvre to understand the condition of the gallery space for ‘inasmuch as abstract space tends towards homogeneity, towards the elimination of existing differences or peculiarities, a new space cannot be born (produced) unless it accentuates differences.’ (Lefebvre: 1991, 52) The work of the artist, by contrast, liberates difference. More specifically, the art of performance simultaneously generates and exposes marginal space within the gallery interior; a corporeal action that deposits residual stains and blemishes across the galleries internal skin, leaving marks and traces that resist homogeneity to create a temporary site of differential experience. The lost, forgotten or overlooked marginal zones and irregularities of a gallery space become a point of ephemeral spectacle and this paper addresses the impact of this spatial and corporeal collision. The research that informs and situates these phenomena traces the irregularities, blemishes and scars that resist conventional mapping; marks that exist within an alternative, unconventional and unbleached space before, during and after a performance act. Recorded through orthographic drawing conventions, the research generated a narrative cartography of corporeal intervention within the interior of X Church Slumgothic, a heavily used semi-decayed community art space in Gainsborough. The co-authors of this research formed a practical collaboration that fused the dynamics and complexities of the performer’s body with the fixed conventions of architectural drawings. The discussion in this paper between performer and draughtsman explores how the body becomes an instrument to record and describe an arts interior beyond, yet from within, traditional architectural systems of representation

Tail bounds for all eigenvalues of a sum of random matrices

This work introduces the minimax Laplace transform method, a modification of the cumulant-based matrix Laplace transform method developed in "User-friendly tail bounds for sums of random matrices" (arXiv:1004.4389v6) that yields both upper and lower bounds on each eigenvalue of a sum of random self-adjoint matrices. This machinery is used to derive eigenvalue analogues of the classical Chernoff, Bennett, and Bernstein bounds. Two examples demonstrate the efficacy of the minimax Laplace transform. The first concerns the effects of column sparsification on the spectrum of a matrix with orthonormal rows. Here, the behavior of the singular values can be described in terms of coherence-like quantities. The second example addresses the question of relative accuracy in the estimation of eigenvalues of the covariance matrix of a random process. Standard results on the convergence of sample covariance matrices provide bounds on the number of samples needed to obtain relative accuracy in the spectral norm, but these results only guarantee relative accuracy in the estimate of the maximum eigenvalue. The minimax Laplace transform argument establishes that if the lowest eigenvalues decay sufficiently fast, on the order of (K^2*r*log(p))/eps^2 samples, where K is the condition number of an optimal rank-r approximation to C, are sufficient to ensure that the dominant r eigenvalues of the covariance matrix of a N(0, C) random vector are estimated to within a factor of 1+-eps with high probability.Comment: 20 pages, 1 figure, see also arXiv:1004.4389v

Revisiting the Nystrom Method for Improved Large-Scale Machine Learning

We reconsider randomized algorithms for the low-rank approximation of symmetric positive semi-definite (SPSD) matrices such as Laplacian and kernel matrices that arise in data analysis and machine learning applications. Our main results consist of an empirical evaluation of the performance quality and running time of sampling and projection methods on a diverse suite of SPSD matrices. Our results highlight complementary aspects of sampling versus projection methods; they characterize the effects of common data preprocessing steps on the performance of these algorithms; and they point to important differences between uniform sampling and nonuniform sampling methods based on leverage scores. In addition, our empirical results illustrate that existing theory is so weak that it does not provide even a qualitative guide to practice. Thus, we complement our empirical results with a suite of worst-case theoretical bounds for both random sampling and random projection methods. These bounds are qualitatively superior to existing bounds---e.g. improved additive-error bounds for spectral and Frobenius norm error and relative-error bounds for trace norm error---and they point to future directions to make these algorithms useful in even larger-scale machine learning applications.Comment: 60 pages, 15 color figures; updated proof of Frobenius norm bounds, added comparison to projection-based low-rank approximations, and an analysis of the power method applied to SPSD sketche

Defining Deviance: Sex, Science, and Delinquent Girls, 1890–1960

Review of: "Defining Deviance: Sex, Science, and Delinquent Girls, 1890–1960," by Michael A. Rembis

The Masked Sample Covariance Estimator: An Analysis via Matrix Concentration Inequalities

Covariance estimation becomes challenging in the regime where the number p of variables outstrips the number n of samples available to construct the estimate. One way to circumvent this problem is to assume that the covariance matrix is nearly sparse and to focus on estimating only the significant entries. To analyze this approach, Levina and Vershynin (2011) introduce a formalism called masked covariance estimation, where each entry of the sample covariance estimator is reweighted to reflect an a priori assessment of its importance. This paper provides a short analysis of the masked sample covariance estimator by means of a matrix concentration inequality. The main result applies to general distributions with at least four moments. Specialized to the case of a Gaussian distribution, the theory offers qualitative improvements over earlier work. For example, the new results show that n = O(B log^2 p) samples suffice to estimate a banded covariance matrix with bandwidth B up to a relative spectral-norm error, in contrast to the sample complexity n = O(B log^5 p) obtained by Levina and Vershynin