144 research outputs found
Ranking-based Deep Cross-modal Hashing
Cross-modal hashing has been receiving increasing interests for its low
storage cost and fast query speed in multi-modal data retrievals. However, most
existing hashing methods are based on hand-crafted or raw level features of
objects, which may not be optimally compatible with the coding process.
Besides, these hashing methods are mainly designed to handle simple pairwise
similarity. The complex multilevel ranking semantic structure of instances
associated with multiple labels has not been well explored yet. In this paper,
we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH
firstly uses the feature and label information of data to derive a
semi-supervised semantic ranking list. Next, to expand the semantic
representation power of hand-crafted features, RDCMH integrates the semantic
ranking information into deep cross-modal hashing and jointly optimizes the
compatible parameters of deep feature representations and of hashing functions.
Experiments on real multi-modal datasets show that RDCMH outperforms other
competitive baselines and achieves the state-of-the-art performance in
cross-modal retrieval applications
Multi-View Multiple Clusterings using Deep Matrix Factorization
Multi-view clustering aims at integrating complementary information from
multiple heterogeneous views to improve clustering results. Existing multi-view
clustering solutions can only output a single clustering of the data. Due to
their multiplicity, multi-view data, can have different groupings that are
reasonable and interesting from different perspectives. However, how to find
multiple, meaningful, and diverse clustering results from multi-view data is
still a rarely studied and challenging topic in multi-view clustering and
multiple clusterings. In this paper, we introduce a deep matrix factorization
based solution (DMClusts) to discover multiple clusterings. DMClusts gradually
factorizes multi-view data matrices into representational subspaces
layer-by-layer and generates one clustering in each layer. To enforce the
diversity between generated clusterings, it minimizes a new redundancy
quantification term derived from the proximity between samples in these
subspaces. We further introduce an iterative optimization procedure to
simultaneously seek multiple clusterings with quality and diversity.
Experimental results on benchmark datasets confirm that DMClusts outperforms
state-of-the-art multiple clustering solutions
Reinforcement Causal Structure Learning on Order Graph
Learning directed acyclic graph (DAG) that describes the causality of
observed data is a very challenging but important task. Due to the limited
quantity and quality of observed data, and non-identifiability of causal graph,
it is almost impossible to infer a single precise DAG. Some methods approximate
the posterior distribution of DAGs to explore the DAG space via Markov chain
Monte Carlo (MCMC), but the DAG space is over the nature of super-exponential
growth, accurately characterizing the whole distribution over DAGs is very
intractable. In this paper, we propose {Reinforcement Causal Structure Learning
on Order Graph} (RCL-OG) that uses order graph instead of MCMC to model
different DAG topological orderings and to reduce the problem size. RCL-OG
first defines reinforcement learning with a new reward mechanism to approximate
the posterior distribution of orderings in an efficacy way, and uses deep
Q-learning to update and transfer rewards between nodes. Next, it obtains the
probability transition model of nodes on order graph, and computes the
posterior probability of different orderings. In this way, we can sample on
this model to obtain the ordering with high probability. Experiments on
synthetic and benchmark datasets show that RCL-OG provides accurate posterior
probability approximation and achieves better results than competitive causal
discovery algorithms.Comment: Accepted by the Thirty-Seventh AAAI Conference on Artificial
Intelligence(AAAI2023
Protein Function Prediction by Integrating Multiple Kernels ∗
Determining protein function constitutes an exercise in integrating information derived from several heterogeneous high-throughput experiments. To utilize the information spread across multiple sources in a combined fashion, these data sources are transformed into kernels. Several protein function prediction methods follow a two-phased approach: they first optimize the weights on individual kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these methods result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some methods optimize the loss of binary classifiers, and learn weights for the different kernels iteratively. A protein has multiple functions, and each function can be viewed as a label. These methods solve the problem of optimizing weights on the input kernels for each of the labels. This is computationally expensive and ignores inter-label correlations. In this paper, we propose a method called Protein Function Prediction by Integrating Multiple Kernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reducing the empirical loss of a multi-label classifier for each of the labels simultaneously, using a combined objective function. ProMK can assign larger weights to smooth kernels and downgrade the weights on noisy kernels. We evaluate the ability of ProMK to predict the function of proteins using several standard benchmarks. We show that our approach performs better than previously proposed protein function prediction approaches that integrate data from multiple networks, and multi-label multiple kernel learning methods.
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