We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

A feature-based model explanation denotes how much each input feature contributes to a model's output for a given data point. As the number of proposed explanation functions grows, we lack quantitative evaluation criteria to help practitioners know when to use which explanation function. This paper proposes quantitative evaluation criteria for feature-based explanations: low sensitivity, high faithfulness, and low complexity. We devise a framework for aggregating explanation functions. We develop a procedure for learning an aggregate explanation function with lower complexity and then derive a new aggregate Shapley value explanation function that minimizes sensitivity.

/pdf/evaluating-and-aggregating-feature-based-model-explanations-37iu1ojf64.pdf

Evaluating and Aggregating Feature-based Model Explanations

Precision medicine and personalized treatment can be effectively achieved through a combination of machine learning ML techniques and data gathered through specialized biosensors. 

However, the biggest challenge appears to be need of better data – in terms of both quality and quantity – on which to apply appropriate ML techniques. While ML can uncover deeper dependencies between the data, it requires enough good data to capture these dependencies. No matter how good the ML algorithms are, they cannot find something which is not in the training set. Hence, better (in terms of both quality and quantity) datasets are required to train algorithms. This, in turn, means that improved biosensors are needed to deliver such comprehensive, specialized data. 

The first section of the report provides a comprehensive literature review of personalized medicine applications employing ML methods on sensor data, under categories of Mobile sensing and portable devices, Neuroimaging, Brain Machine Interface, Omics and electronic health records, and Biosensor systems. 

While the first section is focused on opportunities and successes described in the literature, the second section highlights the key challenges which need to be addressed in order to take full advantage of the benefits ML can offer in the areas of personalized medicine. They also underscore the need for biosensors which provide better quality and quantity of data on which ML may operate. 

The third section describes select “best practices” for ML applications: data quality management and validation, data visualization, and privacy preserving data analysis. Very effective interactive visualization can be delivered through a Shiny framework and R packages, and we present a representative interactive visualizer using real data.

Lastly, the appendices provide additional information and references on data quality procedures, data visualization techniques, and topics in ML.

Personalized Treatment through Biosensors and Machine Learning ML

Deep neural networks have been widely explored and utilised as a useful tool for feature extraction in computer vision and machine learning. It is often observed that the last fully connected (FC) layers of convolutional neural network possess higher discrimination power as compared to the convolutional and maxpooling layers whose goal is to preserve local and low-level information of the input image and down sample it to avoid overfitting. Inspired from the functionality of local binary pattern (LBP) operator, this paper proposes to induce discrimination into the mid layers of convolutional neural network by introducing a discriminatively boosted alternative to pooling (DBAP) layer that has shown to serve as a favourable replacement of early maxpooling layer in a convolutional neural network (CNN). A thorough research of the related works show that the proposed change in the neural architecture is novel and has not been proposed before to bring enhanced discrimination and feature visualisation power achieved from the mid layer features. The empirical results reveal that the introduction of DBAP layer in popular neural architectures such as AlexNet and LeNet produces competitive classification results in comparison to their baseline models as well as other ultra-deep models on several benchmark data sets. In addition, better visualisation of intermediate features can allow one to seek understanding and interpretation of black box behaviour of convolutional neural networks, used widely by the research community.

/pdf/introspective-analysis-of-convolutional-neural-networks-for-16j48gzqqn.pdf

Introspective analysis of convolutional neural networks for improving discrimination performance and feature visualisation

Machine learning models have become more and more complex in order to better approximate complex functions. Although fruitful in many domains, the added complexity has come at the cost of model interpretability. The once popular k-nearest neighbors (kNN) approach, which finds and uses the most similar data for reasoning, has received much less attention in recent decades due to numerous problems when compared to other techniques. We show that many of these historical problems with kNN can be overcome, and our contribution has applications not only in machine learning but also in online learning, data synthesis, anomaly detection, model compression, and reinforcement learning, without sacrificing interpretability. We introduce a synthesis between kNN and information theory that we hope will provide a clear path towards models that are innately interpretable and auditable. Through this work we hope to gather interest in combining kNN with information theory as a promising path to fully auditable machine learning and artificial intelligence.

pdf/natively-interpretable-machine-learning-and-artificial-1fr5b0aa3m.pdf

Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions.

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Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions.