Spectral methods have been widely used to study the structural properties of unlabeled datasets. In this work we describe a clustering approach that exploits the structural properties in the configuration space of objects as well as the spectral sub-space, quite unlike earlier methods. We propose a spectral clustering approach, where we formalize the notion of clusters as vertices of a simplex ...

Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is “relaxed” into a tractable eigenvector problem, and in which the relaxed solution is subsequently “rounded” into an approximate discrete solution to the original problem. In this paper we present a novel margin-based perspective on multiway spectral clust...

In this paper, we propose a novel non-negative matrix factorization (NMF) to the affinity matrix for document clustering, which enforces nonnegativity and orthogonality constraints simultaneously. With the help of orthogonality constraints, this NMF provides a solution to spectral clustering, which inherits the advantages of spectral clustering and presents a much more reasonable clustering int...

The development of advanced laboratory-based imaging hyperspectral sensors, such as SisuCHEMA, has created an opportunity to extract compositional information of mineral mixtures from spectral images. Determining proportions of minerals on rock surfaces based on spectral signature is a challenging approach due to naturally-occurring minerals that exist in the form of intimate mixtures, and grai...

We present a voice morphing strategy that can be used to generate a continuum of accent transformations between a foreign speaker and a native speaker. The approach performs a cepstral decomposition of speech into spectral slope and spectral detail. Accent conversions are then generated by combining the spectral slope of the foreign speaker with a morph of the spectral detail of the native spea...

Methods for spectral analysis of audio signals and their graphical display are widespread. However, assessing music and audio in the visual domain involves a number of challenges in the translation between auditory images into mental or symbolically represented concepts. This paper presents a spectral analysis method that exists entirely in the auditory domain, and results in an auditory presen...

Some M × T modulation matrices for M transmit antennae and T symbol periods, with M = 2, 3, 4 and T = 2, and M = T = 4 are studied. A transmission rate of M symbols per channel use and a transmit diversity order of min(M,T ) are achieved over a quasi-static fading channel when using rotated versions of a multi-dimensional quadratic amplitude modulation with spectral efficiency 2 bits per symbol...

Hyperspectral imaging is a promising non-invasive method for applications in conservation of painting. With its ability to capture both spatial and spectral information which relates to physical characteristics of materials, the identification of pigments and its spatial distribution across the painting is now possible. In this work, The Scream (1893) by Edvard Munch is acquired using a hypersp...

Many approaches to analyzing the structure of a musical recording involve detecting sequential patterns within a selfsimilarity matrix derived from time-series features. Such patterns ideally capture repeated sequences, which then form the building blocks of large-scale structure. In this work, techniques from spectral graph theory are applied to analyze repeated patterns in musical recordings....

Spectral clustering is a flexible clustering methodology that is applicable to a variety of data types and has the particular virtue that it makes few assumptions on cluster shapes. It has become popular in a variety of application areas, particularly in computational vision and bioinformatics. The approach appears, however, to be particularly sensitive to irrelevant and noisy dimensions in the...