LDLLC

SpriteMisaka · Aug 23, 2024 · e9dcc8a · e9dcc8a
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diff --git a/README.md b/README.md
@@ -9,15 +9,16 @@ Label distribution learning (LDL) and label enhancement (LE) toolkit implemented
   + ([Yang, Sun, and Sun 2017](https://doi.org/10.1609/aaai.v31i1.10485)) [*AAAI*]: `BCPNN` and `ACPNN`.
   + ([Xu and Zhou 2017](https://doi.org/10.24963/ijcai.2017/443)) [*IJCAI*]: `IncomLDL`$^2$.
   + ([Shen et al. 2017](https://papers.nips.cc/paper_files/paper/2017/hash/6e2713a6efee97bacb63e52c54f0ada0-Abstract.html)) [*NeurIPS*]: `LDLF`.
-  + ([Wang and Geng 2019](https://doi.org/10.24963/ijcai.2019/515)) [*IJCAI*]: `LDL4C`$^3$.
+  + ([Jia et al. 2018](https://doi.org/10.1609/aaai.v32i1.11664)) [*AAAI*]: $^\dagger$`LDLLC`.
+  + ([Wang and Geng 2019](https://doi.org/10.24963/ijcai.2019/515)) [*IJCAI*]: $^\dagger$`LDL4C`$^3$.
   + ([Shen et al. 2020](https://dx.doi.org/10.14177/j.cnki.32-1397n.2020.44.06.004)) [*南京理工大学学报* (Chinese)]: `AdaBoostLDL`.
   + ([González et al. 2021a](https://doi.org/10.1016/j.ins.2020.07.071)) [*Inf. Sci.*]: `SSG_LDL`$^4$.
   + ([González et al. 2021b](https://doi.org/10.1016/j.inffus.2020.08.024)) [*Inf. Fusion*]: `DF_LDL`.
-  + ([Wang and Geng 2021a](https://doi.org/10.24963/ijcai.2021/426)) [*IJCAI*]: `LDL_HR`$^3$.
-  + ([Wang and Geng 2021b](https://proceedings.mlr.press/v139/wang21h.html)) [*ICML*]: `LDLM`$^3$.
+  + ([Wang and Geng 2021a](https://doi.org/10.24963/ijcai.2021/426)) [*IJCAI*]: $^\dagger$`LDL_HR`$^3$.
+  + ([Wang and Geng 2021b](https://proceedings.mlr.press/v139/wang21h.html)) [*ICML*]: $^\dagger$`LDLM`$^3$.
   + ([Jia et al. 2021](https://doi.org/10.1109/TKDE.2019.2943337)) [*TKDE*]: `LDL_SCL`.
-  + ([Jia et al. 2023a](https://doi.org/10.1109/TKDE.2021.3099294)) [*TKDE*]: `LDL_LRR`.
-  + ([Jia et al. 2023b](https://doi.org/10.1109/TNNLS.2023.3258976)) [*TNNLS*]: `LDL_DPA`.
+  + ([Jia et al. 2023a](https://doi.org/10.1109/TKDE.2021.3099294)) [*TKDE*]: $^\dagger$`LDL_LRR`.
+  + ([Jia et al. 2023b](https://doi.org/10.1109/TNNLS.2023.3258976)) [*TNNLS*]: $^\dagger$`LDL_DPA`.
   + ([Wen et al. 2023](https://doi.org/10.1109/ICCV51070.2023.02146)) [*ICCV*]: `CAD`$^1$, `QFD2`$^1$, and `CJS`$^1$.
   + ([Li and Chen 2024](https://doi.org/10.24963/ijcai.2024/494)) [*IJCAI*]: `WInLDL`$^2$.
   + ([Wu, Li, and Jia 2024](https://doi.org/10.1109/TBDATA.2024.3442562)) [*TBD*]: `LDL_DA`$^5$.
@@ -28,7 +29,7 @@ Label distribution learning (LDL) and label enhancement (LE) toolkit implemented
 + LDL metrics: `chebyshev`, `clark`, `canberra`, `kl_divergence`, `cosine`, `intersection`, etc.
 + Structured LDL datasets: *Human_Gene*, *Movie*, *Natural_Scene*, *s-BU_3DFE*, *s-JAFFE*, *Yeast*, etc.
 + LDL applications:
-  + Facial emotion recognition (supported datasets: [*JAFFE*](https://zenodo.org/records/3451524) and [*BU-3DFE*](https://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html))
+  + Facial emotion recognition (supported datasets: [*JAFFE*](https://zenodo.org/records/3451524) and [*BU-3DFE*](https://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html)).
   + ([Shirani et al. 2019](https://doi.org/10.18653/v1/P19-1112)) [*ACL*]: Emphasis selection (supported datasets: [*SemEval2020*](https://github.com/RiTUAL-UH/SemEval2020_Task10_Emphasis_Selection); pre-trained GloVe embeddings can be downloaded [here](https://nlp.stanford.edu/projects/glove/)).
   + ([Wu et al. 2019](https://doi.org/10.1109/ICCV.2019.01074)) [*ICCV*]: Lesion counting (supported datasets: [*ACNE04*](https://drive.google.com/drive/folders/18yJcHXhzOv7H89t-Lda6phheAicLqMuZ)).
   + ([Chen et al. 2020](https://doi.org/10.1109/CVPR42600.2020.01400)) [*CVPR*]: Facial emotion recognition with auxiliary label space graphs (supported datasets: [*CK+*](https://www.jeffcohn.net/Resources/); OpenFace can be downloaded [here](https://github.com/TadasBaltrusaitis/OpenFace/releases), and the required models can be downloaded [here](https://github.com/TadasBaltrusaitis/OpenFace/wiki/Model-download)).
@@ -41,7 +42,9 @@ Label distribution learning (LDL) and label enhancement (LE) toolkit implemented
 >
 > $^4$ These are oversampling algorithms for LDL, therefore you should use `fit_transform` to generate synthetic samples.
 >
-> $^5$: To use domain adaptation methods for LDL, you need to provide the source domain data via parameters `sX` and `sy` of the `fit` method. [Here](https://github.com/SpriteMisaka/PyLDL/blob/main/demo/domain_adaptation.ipynb) is a demo on domain adaptation for LDL.
+> $^5$ To use domain adaptation methods for LDL, you need to provide the source domain data via parameters `sX` and `sy` of the `fit` method. [Here](https://github.com/SpriteMisaka/PyLDL/blob/main/demo/domain_adaptation.ipynb) is a demo on domain adaptation for LDL.
+
+> $^\dagger$ These methods involve imposing constraints on model parameters, like regularization. Therefore, it is recommended to carefully tune the hyperparameters and apply feature preprocessing techniques like `StandardScaler` or `MinMaxScaler` before conducting experiments to achieve the expected performance.
 
 ## Installation
 

diff --git a/docs/genindex.html b/docs/genindex.html
@@ -98,6 +98,8 @@ <h2 id="L">L</h2>
       <li><a href="index.html#pyldl.algorithms.LDL_SCL">LDL_SCL (class in pyldl.algorithms)</a>
 </li>
       <li><a href="index.html#pyldl.algorithms.LDLF">LDLF (class in pyldl.algorithms)</a>
+</li>
+      <li><a href="index.html#pyldl.algorithms.LDLLC">LDLLC (class in pyldl.algorithms)</a>
 </li>
       <li><a href="index.html#pyldl.algorithms.LDLM">LDLM (class in pyldl.algorithms)</a>
 </li>
@@ -127,8 +129,6 @@ <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#pyldl.algorithms.LDL_DA.pairwise_cosine">pairwise_cosine() (pyldl.algorithms.LDL_DA static method)</a>
-</li>
-      <li><a href="index.html#pyldl.algorithms.LDL_DA.pairwise_euclidean">pairwise_euclidean() (pyldl.algorithms.LDL_DA static method)</a>
 </li>
       <li><a href="index.html#pyldl.algorithms.LDL_DA.pairwise_jsd">pairwise_jsd() (pyldl.algorithms.LDL_DA static method)</a>
 </li>

diff --git a/docs/index.html b/docs/index.html
@@ -54,6 +54,12 @@ <h1>PyLDL documentation<a class="headerlink" href="#pyldl-documentation" title="
 <dd><p>LDLF is proposed in paper <em>Label Distribution Learning Forests</em>.</p>
 </dd></dl>
 
+<dl class="py class">
+<dt class="sig sig-object py" id="pyldl.algorithms.LDLLC">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyldl.algorithms.</span></span><span class="sig-name descname"><span class="pre">LDLLC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyldl.algorithms.LDLLC" title="Link to this definition">¶</a></dt>
+<dd><p>LDLLC is proposed in paper <em>Label Distribution Learning by Exploiting Label Correlations</em>.</p>
+</dd></dl>
+
 <dl class="py class">
 <dt class="sig sig-object py" id="pyldl.algorithms.LDLM">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyldl.algorithms.</span></span><span class="sig-name descname"><span class="pre">LDLM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyldl.algorithms.LDLM" title="Link to this definition">¶</a></dt>
@@ -133,26 +139,6 @@ <h1>PyLDL documentation<a class="headerlink" href="#pyldl-documentation" title="
 </dl>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="pyldl.algorithms.LDL_DA.pairwise_euclidean">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">pairwise_euclidean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#pyldl.algorithms.LDL_DA.pairwise_euclidean" title="Link to this definition">¶</a></dt>
-<dd><p>Pairwise Euclidean distance.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<em>tf.Tensor</em>) – Matrix <span class="math notranslate nohighlight">\(\boldsymbol{X}\)</span> (shape: <span class="math notranslate nohighlight">\([m_X,\, n_X]\)</span>).</p></li>
-<li><p><strong>Y</strong> (<em>tf.Tensor</em>) – Matrix <span class="math notranslate nohighlight">\(\boldsymbol{Y}\)</span> (shape: <span class="math notranslate nohighlight">\([m_Y,\, n_Y]\)</span>).</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Pairwise Euclidean distance (shape: <span class="math notranslate nohighlight">\([m_X,\, m_Y]\)</span>).</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>tf.Tensor</p>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py method">
 <dt class="sig sig-object py" id="pyldl.algorithms.LDL_DA.pairwise_jsd">
 <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">pairwise_jsd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#pyldl.algorithms.LDL_DA.pairwise_jsd" title="Link to this definition">¶</a></dt>

diff --git a/docs/objects.inv b/docs/objects.inv
@@ -2,6 +2,9 @@
 # Project: PyLDL
 # Version: 
 # The remainder of this file is compressed using zlib.
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