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Found 14 papers, 1 papers with code
Breaking the curse of dimensionality with Isolation Kernel
The curse of dimensionality has been studied in different aspects.
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection
Existing approaches based on kernel mean embedding, which convert a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; and it is {\em data independent}.
Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning
A current key approach focuses on ways to produce an approximate finite-dimensional feature map, assuming that the kernel used has a feature map with intractable dimensionality---an assumption traditionally held in kernel-based methods.
A new simple and effective measure for bag-of-word inter-document similarity measurement
To measure the similarity of two documents in the bag-of-words (BoW) vector representation, different term weighting schemes are used to improve the performance of cosine similarity---the most widely used inter-document similarity measure in text mining.
Learning Graph Representation via Formal Concept Analysis
We present a novel method that can learn a graph representation from multivariate data.
Analysis of cause-effect inference by comparing regression errors
We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions.
Error Asymmetry in Causal and Anticausal Regression
It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated.
A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes
A large amount of observational data has been accumulated in various fields in recent times, and there is a growing need to estimate the generating processes of these data.
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence.
Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery
The notion of causality is used in many situations dealing with uncertainty.
Anomaly detection in reconstructed quantum states using a machine-learning technique
We demonstrate that the proposed method can more accurately detect small erroneous deviations in reconstructed density matrices, which contain intrinsic fluctuations due to the limited number of samples, than a naive method of checking the trace distance from the average of the given density matrices.
ParceLiNGAM: A causal ordering method robust against latent confounders
In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders.
Weighted Likelihood Policy Search with Model Selection
Reinforcement learning (RL) methods based on direct policy search (DPS) have been actively discussed to achieve an efficient approach to complicated Markov decision processes (MDPs).
