Multi-label learning by exploiting label dependency

By Min-Ling Zhang, Kun Zhang. Knowledge Discovery and Data Mining, 2010

474 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/53f83c8fabe98aace1429a7eb38006d935be49fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

Multilabel Image Classification With Regional Latent Semantic Dependencies (2016, methodology): “In [33], joint probability is exploited by directed acyclic graph and chain rules.”
Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification (2018, methodology): “Although several methods of object classification have managed to model correlations between attributes [46], [47], [48], as far as we know, no work has utilized both low-level features and attribute correlations across cameras for re-identification in a systematic manner.”
Multi-Label Learning with Global and Local Label Correlation (2017, background): “These datasets have been widely used in multi-label learning literatures [7], [8], [11], [14], [15], [22], [26], [36], [37], [38], [39], [40], [41], [42] etc.”
A Review on Multi-Label Learning Algorithms (2014, background): “The most prominent advantage of Binary Relevance lies in its extremely straightforward way of handling multi-label data (Steps 1-4), which has been employed as the building block of many state-of-the-art multi-label learning techniques [20], [34], [72], [106].”
A Literature Survey on Algorithms for Multi-label Learning (2010, methodology): “1 Distinct Label Set (DL) is the total count of number of distinct label combinations observed in the dataset [67].”
Statistical topic models for multi-label document classification (2011, background): “Zhang and Zhang (2010) present a hybrid generative-discriminative approach to multi-label classification.”
Multi-Task Learning with Low Rank Attribute Embedding for Person Re-Identification (2015, background): “Although a few approaches to object classification have modeled attribute correlations [18, 37, 46], to the best of our knowledge, no work has utilized both low level features and attribute correlations across cameras for re-identification in a principled way.”
A Survey on Text Classification: From Shallow to Deep Learning (2020, background): “Probabilistic graphical models (PGMs) express the conditional dependencies among features in graphs, such as the Bayesian network [96], the hidden Markov network [97].”
Lift: Multi-Label Learning with Label-Specific Features (2011, methodology): “Specifically, a family of q functions {f1, f2, · · · , fq} are learned by exploring the influences among a subset of functions {fk1 , fk2 , · · · , fkq′ } (q ′ ≤ q) [Tsoumakas and Vlahavas, 2007; Read et al., 2009; Zhang and Zhang, 2010] or among all the functions [Yan et al.”
Deep Extreme Multi-label Learning (2017, methodology): “Early MLC methods [Boutell et al. , 2004] transform the MLC problem either into one or more single-label classiﬁcation or regression problems.”
Binary relevance for multi-label learning: an overview (2018, methodology): “Firstly, tree-based Table 4 The pseudo-code of LEAD [25].”
On label dependence and loss minimization in multi-label classification (2012, background): “A less general statement has been put forward in Dembczyński et al. (2010b), and independently in Zhang and Zhang (2010).”
A Survey on Text Classification: From Traditional to Deep Learning (2020, background): “Probabilistic Graphical Models (PGMs) express the conditional dependencies among features in graphs, such as the Bayesian network [25].”