Learning Graph Quantization
نویسندگان
چکیده
This contribution extends learning vector quantization to the domain of graphs. For this, we first identify graphs with points in some orbifold, then derive a generalized differentiable intrinsic metric, and finally extend the update rule of LVQ for generalized differentiable distance metrics. First experiments indicate that the proposed approach can perform comparable to state-of-the-art methods in structural pattern recognition.
منابع مشابه
Generalized Learning Graph Quantization
This contribution extends generalized LVQ, generalized relevance LVQ, and robust soft LVQ to the graph domain. The proposed approaches are based on the basic learning graph quantization (lgq) algorithm using the orbifold framework. Experiments on three data sets show that the proposed approaches outperform lgq and lgq2.1.
متن کاملAccelerating Competitive Learning Graph Quantization
Vector quantization(VQ) is a lossy data compression technique from signal processing for which simple competitive learning is one standard method to quantize patterns from the input space. Extending competitive learning VQ to the domain of graphs results in competitive learning for quantizing input graphs. In this contribution, we propose an accelerated version of competitive learning graph qua...
متن کاملGraph quantization
Vector quantization(VQ) is a lossy data compression technique from signal processing, which is restricted to feature vectors and therefore inapplicable for combinatorial structures. This contribution aims at extending VQ to the quantization of graphs in a theoretically principled way in order to overcome practical limitations known in the context of prototype-based clustering of graphs. For thi...
متن کاملWeakly supervised codebook learning by iterative label propagation with graph quantization
Visual codebook serves as a fundamental component in many state-of-the-art visual search and object recognition systems. While most existing codebooks are built based solely on unsupervised patch quantization, there are few works exploited image labels to supervise its construction. The key challenge lies in the following: image labels are global, but patch supervision should be local. Such imb...
متن کاملIncremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning
While the harmonic function solution performs well in many semi-supervised learning (SSL) tasks, it is known to scale poorly with the number of samples. Recent successful and scalable methods, such as the eigenfunction method [11] focus on efficiently approximating the whole spectrum of the graph Laplacian constructed from the data. This is in contrast to various subsampling and quantization me...
متن کامل