• TOAIJ-4-1_F2

    A Novel Antibiotic Spacer for Significant Proximal Femoral

    Infections of proximal femora with prosthetic implants in situ have long been a major concern in orthopedic surgery. The gold standard in the management of infected proximal femurs in the presence of prosthetic implants has traditionally been a two-stage revision.

  • TOAIJ-4-1_F3

    Challenges and Solutions in Management of Distal Humerus Fractures

    Management of distal humerus fractures remains a challenge for trauma surgeons and advancements in treatment options continue to be made to achieve the best results for patients presenting with these complex fractures.

  • TOAIJ-4-20_F1

    Management of Glenoid Defects in Anterior Shoulder Instability

    Bone defects of the glenoid are often found in patients with traumatic anterior glenohumeral instability. There is no consensus regarding which glenoid defects need to be treated surgically. The aim of this review is to describe the management of glenoid defects in anterior shoulder instability in patients with traumatic anterior glenohumeral instability.

  • TOAIJ-4-20_F2

    Hallux Osteoid Osteoma: A Case Report and Literature Review

    Osteoid osteoma is a benign bone tumour that mostly affects males in the second and third decade of their life. The lesion mainly occurs in long bones, usually in the femur and tibia, causing severe localized pain that is worse at night and responds to nonsteroidal anti-inflammatory drugs (NSAIDs).

Editor's Choice

Margin Based Dimensionality Reduction and Generalization

Jing Peng , Stefan Robila , Wei Fan , Guna Seetharaman

Linear discriminant analysis (LDA) for dimension reduction has been applied to a wide variety of problems such as face recognition. However, it has a major computational difficulty when the number of dimensions is greater than the sample size. In this paper, we propose a margin based criterion for linear dimension reduction that addresses the above problem associated with LDA. We establish an error bound for our proposed technique by showing its relation to least squares regression. In addition, there are well established numerical procedures such as semi-definite programming for optimizing the proposed criterion. We demonstrate the efficacy of our proposal and compare it against other competing techniques using a number of examples.


August 24, 2010
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