Pairwise-comparison estimation with non-parametric controls

Non-Parametric Kernel Learning with robust pairwise constraints

For existing kernel learning based semi-supervised clustering algorithms, it is generally difficult to scale well with large scale datasets and robust pairwise constraints. In this paper, we proposed a new Non-Parametric Kernel Learning framework (NPKL) to deal with these problems. We generalized the graph embedding framework into kernel learning, by reforming it as a semi-definitive programmin...

Fully-Connected CRFs with Non-Parametric Pairwise Potentials

Conditional Random Fields (CRFs) are used for diverse tasks, ranging from image denoising to object recognition. For images, they are commonly defined as a graph with nodes corresponding to individual pixels and pairwise links that connect nodes to their immediate neighbors. Recent work has shown that fully-connected CRFs, where each node is connected to every other node, can be solved efficien...

Fully-Connected CRFs with Non-Parametric Pairwise Potentials Supplementary Material

Landmark Multidimensional Scaling (LMDS) The O(N) memory complexity of cMDS makes it an impractical choice when N is the number of pixels. Randomized approaches sample rows of the distance matrix to build approximate representatives of the entire matrix [4]. Depending on the coherence of the right singular vectors of D, it has been shown [2] that the Nyström approximation is the original matrix...

Tremor Detection Using Parametric and Non-Parametric Spectral Estimation Methods: A Comparison with Clinical Assessment

In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a better insight into tremor. Typically, routine clinical assessment of accelerometry and electromy...

Non–parametric volatility estimation