[Swarm-Modelling] Neurocomputing Special Issue on Subspace Learning

[Dacheng Tao]

Neurocomputing Special Issue on Subspace Learning

Subspace learning, a typical neurocomputing technique, sheds light on various tasks from computer vision to data mining. A subspace learning algorithm projects the original high dimensional feature space to a low dimensional subspace, wherein specific statistical properties can be well preserved. For example, Fisher’s linear discriminant analysis preserves the classification structure in the low dimensional projected subspace by assuming samples are drawn from homoscedastic unimodal Gaussians.

The past years witnessed very significant contributions of subspace learning for various applications, e.g., face identification and authentication, human gait analysis, video tracking, document classification, scene understanding, object categorization, and multimedia information retrieval. Subspace learning has been furthered from relevant research fields, e.g., nonparametric Bayesian analysis, manifold learning, spectral analysis, kernel machine, tensor machine, and incremental learning. As a consequence, subspace learning is a never‐ending resilience field and attracts growing efforts from different fields.

Elsevier Neurocomputing hunts for original research results for a Special Issue on Subspace Learning. The goals of this special issue are twofold: 1) developing subspace learning algorithms to target specific applications and 2) defining applications, which can be cleared up by subspace learning algorithms.

Manuscripts are solicited to address a wide range of topics in subspace learning, but not limit to the following:

- Extensions of Fisher’s linear discriminant analysis and other traditional subspace learning algorithms, e.g., principal component analysis.

- Spectral analysis

- Probabilistic graphical models

- Manifold learning

- Kernel machines and tensor machines

- Supervised, semi‐supervised and unsupervised subspace analysis

- Sparse analysis

- Subspace learning for biometrics, e.g., face.

- Subspace learning for multimedia information retrieval, e.g., relevance feedback and active sample selection.

- Subspace learning for video surveillance, e.g., video tracking, motion analysis and background modeling.

Manuscripts (6‐15 pages in the Neurocomputing publishing format) should be submitted via the Electronic Editorial System, Elsevier: http://ees.elsevier.com/neucom/

Important: when submitting your manuscript, at the step of “Selecting an Article Type is Required for Submission”, please indicate: “Special Issue: Subspace Learning”

Important Dates

Manuscript submission: 10 May 2009

Preliminary results: 10 July 2009

Revised version: 10 August 2009

Notification: 10 October 2009

Final manuscripts due: 10 November 2009

Anticipated publication: Spring 2010

Guest editors:

Xuelong Li

Birkbeck College, University of London

address@hidden

Dacheng Tao

Nanyang Technological University

address@hidden