Conditional t-SNE (ct-SNE) is a recent extension to that allows removal of known cluster information from the embedding, obtain visualization revealing structure beyond label information. This useful, for example, when one wants factor out unwanted differences between set classes. We show ct-SNE fails in many realistic settings, namely if data well clustered over labels original high-dimensiona...

Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We employ a Convolutional Neural Network (CNN) to encode plain text into a fixed...

In this paper, we study the few-shot multi-label classification for user intent detection. For detection, state-of-the-art work estimates label-instance relevance scores and uses a threshold to select multiple associated labels. To determine appropriate thresholds with only few examples, first learn universal thresholding experience on data-rich domains, then adapt certain domains calibration b...

Abstract With the rapid development of information technology, a large amount unlabeled high-dimensional data has been generated. To be able to better handle these data, we propose new self-supervised feature selection algorithm for spectral embedding based on block HSIC lasso (FSSBH). It innovatively applies theoretical approach scenarios importance assessment, and performs by learning with ps...

Relation extraction is a crucial task in natural language processing (NLP) that aims to extract all relational triples from given sentence. Extracting overlapping complex texts challenging and has received extensive research attention. Most existing methods are based on cascade models employ transform the sentence into vectorized representations. The cascaded structure can cause exposure bias i...

Imbalanced training data always puzzles the supervised learning based emotion and sentiment classification. Several existing research showed that data sparseness and small disjuncts are the two major factors affecting the classification. Target to these two problems, this paper presents a word embedding based oversampling method. Firstly, a large-scale text corpus is used to train a continuous ...

Existing zero-shot learning (ZSL) methods usually learn a projection function between a feature space and a semantic embedding space(text or attribute space) in the training seen classes or testing unseen classes. However, the projection function cannot be used between the feature space and multi-semantic embedding spaces, which have the diversity characteristic for describing the different sem...

We present an approach to find the edge congestion sum and dilation sum forembedding of square of cycle on n vertices, Cn , and Cn 2 −1 + K1 into arbitrary tree. The embedding algorithms use a technique based on consecutive label property. Our algorithm calculates edge congestion in linear time.

We propose a new approach to the task of fine grained entity type classifications based on label embeddings that allows for information sharing among related labels. Specifically, we learn an embedding for each label and each feature such that labels which frequently co-occur are close in the embedded space. We show that it outperforms state-of-the-art methods on two fine grained entity-classif...