Robust Quantization for General Similarity Search.

Area of Science:

• Computer Science
• Machine Learning
• Data Mining

Background:

• Vector Quantization (VQ) is crucial for efficient similarity search, reducing storage and search complexity by partitioning feature spaces.
• Existing VQ methods struggle with noisy data and are limited to L2-norm distortion, restricting their application scope.
• The L2-norm (squared Euclidean) loss function in VQ is sensitive to noise and lacks generalization capabilities.

Purpose of the Study:

• To propose a novel robust and general VQ framework (RGVQ) that enhances both the robustness and generalization of VQ approaches.
• To address the limitations of L2-norm distortion in VQ by introducing a more versatile Lp,q-norm loss function.
• To develop an efficient optimization scheme for minimizing the non-smooth and non-convex Lp,q-norm function in VQ.

Main Methods:

• Introduced a novel Lp,q-norm loss function to enable Lp-norm similarity search and enhance robustness through q-th order loss.
• Developed a new optimization scheme to effectively minimize the challenging orthogonality constrained Lp,q-norm function.
• Theoretically proved the convergence of the proposed optimization scheme for VQ applications.

Main Results:

• The proposed RGVQ framework significantly improves VQ performance, particularly in scenarios with noisy data.
• Experiments on benchmark datasets demonstrate the superiority of RGVQ over traditional VQ approaches.
• The Lp,q-norm loss function provides greater flexibility and robustness compared to the standard L2-norm.

Conclusions:

• RGVQ offers a more robust and general solution for efficient similarity search, overcoming the limitations of existing VQ methods.
• The novel Lp,q-norm loss function and optimization scheme are effective in handling noisy data and expanding VQ applications.
• This framework advances the field of large-scale similarity search by providing enhanced accuracy and broader applicability.