<p>At CNN, it is common to do dimensionality reduction  with a kernel size of <code>1x1</code>. Thereby only the filter/feature map dimension is affected and the spatial information is kept intact, because the input is 1:1 mapped to the output. </p>

<p>Hereby a good example is the <a href="https://arxiv.org/pdf/1409.4842.pdf" rel="nofollow noreferrer">Inception</a> architecture, which uses 1x1 convolutions to reduce the dimensionality in the inception modules. </p>
Conv1D as dimensionality reduction for LSTM
<p>I was hoping to use CNN as a dimensionality reduction for my LSTM layers.</p>

<p>I have a panel dataset as the following:</p>

<pre><code>sequence of days = 5065
lags = 14 days (those are time series lags)
features = 2767
</code></pre>

<p>Thus, <code>[5065, 14, 2767]</code></p>

<p>As you can see I have more than half as many features as data points, and I wanted to reduce that. Ideally, I wanted to feed my LSTM layers with compressed feature information with something like 32 features, hopefully in the following shape:</p>

<pre><code>[5065, 14, 32]
</code></pre>

<p>However, when setting up the CNN, I understand that filters should be 32, but what about my kernel size? I'm not sure I'm doing the right thing.</p>